From 8372e2da8a99ea79e7cefe5185819ecf13744332 Mon Sep 17 00:00:00 2001
From: yifanmai <185227+yifanmai@users.noreply.github.com>
Date: Wed, 3 Jun 2026 22:35:59 +0000
Subject: [PATCH] Build frontend

---
 .../{index-6sfOSsz5.js => index-C3aFzfEs.js}  |  4 +--
 .../{react-BhnNyHuP.js => react-D3IQTVQZ.js}  | 26 +++++++++----------
 ...kIsf0iIR.js => react-markdown-DTLembof.js} |  2 +-
 ...harts-Bmm96ixf.js => recharts-DhnSad7c.js} |  2 +-
 ...{tremor-DyW3D1Ox.js => tremor-B1ftEuaD.js} |  2 +-
 src/helm/benchmark/static_build/index.html    | 10 +++----
 6 files changed, 23 insertions(+), 23 deletions(-)
 rename src/helm/benchmark/static_build/assets/{index-6sfOSsz5.js => index-C3aFzfEs.js} (71%)
 rename src/helm/benchmark/static_build/assets/{react-BhnNyHuP.js => react-D3IQTVQZ.js} (78%)
 rename src/helm/benchmark/static_build/assets/{react-markdown-kIsf0iIR.js => react-markdown-DTLembof.js} (99%)
 rename src/helm/benchmark/static_build/assets/{recharts-Bmm96ixf.js => recharts-DhnSad7c.js} (99%)
 rename src/helm/benchmark/static_build/assets/{tremor-DyW3D1Ox.js => tremor-B1ftEuaD.js} (99%)

diff --git a/src/helm/benchmark/static_build/assets/index-6sfOSsz5.js b/src/helm/benchmark/static_build/assets/index-C3aFzfEs.js
similarity index 71%
rename from src/helm/benchmark/static_build/assets/index-6sfOSsz5.js
rename to src/helm/benchmark/static_build/assets/index-C3aFzfEs.js
index 1ffd6c3925f..7d0cbb4e4ff 100644
--- a/src/helm/benchmark/static_build/assets/index-6sfOSsz5.js
+++ b/src/helm/benchmark/static_build/assets/index-C3aFzfEs.js
@@ -1,2 +1,2 @@
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Like all other HELM leaderboards, the HELM Capabilities leaderboard provides full prompt-level transparency, and the results can be fully reproduced using the HELM framework."]}),e.jsxs("div",{className:"flex flex-row justify-center my-4",children:[e.jsx("a",{href:"https://crfm.stanford.edu/2025/03/20/helm-capabilities.html",className:"px-10 btn rounded-md mx-4",children:"Blog Post"}),e.jsx(b,{to:"leaderboard",className:"px-10 btn rounded-md mx-4",children:"Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Ut(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"MMLU-Winogrande-Afr: Clinical MMLU and Winogrande in 11 low-resource African languages"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://ghamut.com/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Ghamut Corporation"})," ","and the"," ",e.jsx("a",{href:"https://www.gatesfoundation.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Gates Foundation"}),"."]}),e.jsx("p",{className:"my-4",children:"Large Language Models (LLMs) have shown remarkable performance across various tasks, yet significant disparities remain for non-English languages, and especially native African languages. This paper addresses these disparities by creating approximately 1 million human-translated words of new benchmark data in 8 low-resource African languages, covering a population of over 160 million speakers of: Amharic, Bambara, Igbo, Sepedi (Northern Sotho), Shona, Sesotho (Southern Sotho), Setswana, and Tsonga. Our benchmarks are translations of Winogrande and three sections of MMLU: college medicine, clinical knowledge, and virology. Using the translated benchmarks, we report previously unknown performance gaps between state-of-the-art (SOTA) LLMs in English and African languages. The publicly available benchmarks, translations, and code from this study support further research and development aimed at creating more inclusive and effective language technologies."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2412.12417",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function qt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"SEA-HELM: Southeast Asian Holistic Evaluation of Language Models"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4 italic",children:["This leaderboard is a collaboration with"," ",e.jsx("a",{href:"https://aisingapore.org/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore"}),"."]}),e.jsxs("p",{className:"my-4",children:["With the rapid emergence of novel capabilities in Large Language Models (LLMs), the need for rigorous multilingual and multicultural benchmarks that are integrated has become more pronounced. Though existing LLM benchmarks are capable of evaluating specific capabilities of LLMs in English as well as in various mid- to low-resource languages, including those in the Southeast Asian (SEA) region, a comprehensive and authentic evaluation suite for the SEA languages has not been developed thus far. Here, we present"," ",e.jsx("strong",{className:"font-bold",children:"SEA-HELM"}),", a holistic linguistic and cultural LLM evaluation suite that emphasizes SEA languages, comprising five core pillars: (1) NLP Classics, (2) LLM-specifics, (3) SEA Linguistics, (4) SEA Culture, (5) Safety. SEA-HELM currently supports Filipino, Indonesian, Tamil, Thai, and Vietnamese. We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner."]}),e.jsxs("p",{className:"mb-4 italic",children:["Additional evaluation results are available on the external"," ",e.jsx("a",{href:"https://leaderboard.sea-lion.ai/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore (AISG) SEA-HELM leaderboard"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2502.14301",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"HELM Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://leaderboard.sea-lion.ai/",children:"AISG Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Wt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Speech"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsx("p",{className:"my-4",children:"We present a HELM leaderboard for speech tasks."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function zt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Long Context"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsx("p",{className:"my-4",children:"Recent Large Language Models (LLMs) support processing long inputs with hundreds of thousands or millions of tokens. Long context capabilities are important for many real-world applications, such as processing long text documents, conducting long conversations or following complex instructions. However, support for long inputs does not equate to strong long context capabilities. As such, there is a need for rigorous and comprehensive evaluations of long context capabilities."}),e.jsxs("p",{className:"my-4",children:["To address this, we introduce the"," ",e.jsx("strong",{className:"font-bold",children:"HELM Long Context"})," ","leaderboard, which provides transparent, comparable and reproducible evaluations of long context capabilities of recent models. The benchmark consists of 5 tasks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_squad",children:e.jsx("strong",{className:"font-bold",children:"RULER SQuAD"})})," ","— open ended single-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_hotpotqa",children:e.jsx("strong",{className:"font-bold",children:"RULER HotPotQA"})})," ","— open ended multi-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_qa",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.MC"})})," ","— multiple choice question answering based on the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_sum",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.Sum"})})," ","— summarization of the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/openai_mrcr",children:e.jsx("strong",{className:"font-bold",children:"OpenAI MRCR"})})," ","— multi-round co-reference resolution on a long, multi-turn, synthetic conversation"]})]}),e.jsx("p",{className:"my-4",children:"The results demonstrate that even though significant progress has been made on long context capabilities, there is still considerable room for improvement."}),e.jsx("p",{className:"my-4",children:"As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are reproducible using the HELM open source framework."}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.lvmh.com/",children:"LVMH"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/09/29/helm-long-context.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Vt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM SQL"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4",children:["Text-to-SQL is the task of converting natural language instructions into SQL code. There has been increasing interest in text-to-SQL for applications by data scientists in various domains. Thus, we introduce the ",e.jsx("strong",{className:"font-bold",children:"HELM SQL"})," ","leaderboard for text-to-SQL evaluations. The HELM SQL leaderboard evaluates leading LLMs on two existing text-to-SQL benchmarks (Spider, BIRD-SQL) that cover a range of professional domains. In addition, we introduce a new benchmark,"," ",e.jsx("strong",{className:"font-bold",children:"CzechBankQA"}),", a text-to-SQL benchmark based on a real public bank customer relational database, to address the lack of coverage of text-to-SQL in the financial domain. CzechBankQA consists of text-to-SQL queries and gold labels provided by professionals at Wells Fargo. We hope that this leaderboard provides useful insights for data science practitioners."]}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.wellsfargo.com/",children:"Wells Fargo"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4 hidden",href:"#",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Jt(){const s=e.jsx("strong",{className:"font-bold",children:"ViLLM"});return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"ViLLM: Crossing Linguistic Horizon"}),e.jsxs("p",{className:"text-xl my-4 italic text-center",children:[s," is a comprehensive benchmark suite for evaluating the performance of language models in ",e.jsx("strong",{children:"Vietnamese"}),"."]}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"my-4",children:["As multilingual large language models (LLMs) continue to advance natural language processing, bridging communication across diverse cultures and languages, their effectiveness in lower-resourced languages like Vietnamese remains limited. Despite being trained on large multilingual corpora, most open-source LLMs struggle with Vietnamese understanding and generation.",e.jsx("strong",{children:" ViLLM"})," addresses this gap by providing a robust evaluation framework tailored specifically for Vietnamese. It includes ",e.jsx("strong",{children:"11 essential scenarios"}),", each targeting a core capability of Vietnamese LLMs:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"ViLLM"})," includes 11 carefully designed evaluation scenarios, each addressing a core language modeling capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Question Answering:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/juletxara/xquad_xtreme",children:"XQuAD"}),","," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/facebook/mlqa",children:"MLQA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Summarization:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/Yuhthe/vietnews",children:"VietNews"}),","," 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language models:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Bias Assessment:"})," Detects and mitigates biased patterns in model outputs."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Toxicity Assessment:"})," Monitors and controls the generation of harmful or offensive content."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Fairness Evaluation:"})," Ensures equitable performance across demographic groups and languages."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Robustness Analysis:"})," Evaluates model stability against noisy or adversarial inputs in real-world scenarios."]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://aclanthology.org/2024.findings-naacl.182",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full 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continue to evolve and transform how we interact with technology, there is a growing need for standardized evaluation frameworks that can assess their capabilities across diverse languages and tasks. Despite significant advances in speech processing, many models struggle with multilingual understanding, accent recognition, and domain-specific speech tasks."," ",e.jsx("strong",{children:"SLPHelm"})," addresses these challenges by providing a robust evaluation framework that includes",e.jsx("strong",{children:" 5 key scenarios"})," across ",e.jsx("strong",{children:"15 models"}),", each targeting essential speech processing capabilities:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," includes 5 carefully designed evaluation scenarios, each addressing a core speech processing capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Transcription Accuracy"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Type Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Symptom Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis via Transcription"})})]})]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," evaluates models using comprehensive datasets:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmUltraSuite",children:"UltraSuite"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"ENNI:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/ENNI",children:"ENNI"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"LeNormand:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/LeNormand",children:"LeNormand"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"PERCEPT-GFTA:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/PERCEPT-GFTA",children:"PERCEPT-GFTA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite w/ Manual Labels:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmManualLabels",children:"SLPHelmManualLabels"})]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const Qt=""+new URL("audio-table-Dn5NMMeJ.png",import.meta.url).href;function Zt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:"Holistic Evaluation of Audio-Language Models"}),e.jsxs("div",{className:"flex flex-col sm:flex-row justify-center gap-2 sm:gap-8 md:gap-32 my-8",children:[e.jsx("a",{className:"px-10 btn rounded-md",href:"https://arxiv.org/abs/2508.21376",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"#/leaderboard",children:"Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://github.com/stanford-crfm/helm",children:"Github"})]}),e.jsxs("p",{className:"my-4",children:["Evaluations of"," ",e.jsx("strong",{className:"font-bold",children:"audio-language models (ALMs)"})," ","— multimodal models that take interleaved audio and text as input and output text — are hindered by the lack of standardized benchmarks; most benchmarks measure only one or two capabilities and omit evaluative aspects such as fairness or safety. Furthermore, comparison across models is difficult as separate evaluations test a limited number of models and use different prompting methods and inference parameters."]}),e.jsxs("p",{className:"my-4",children:["To address these shortfalls, we introduce"," ",e.jsx("strong",{className:"font-bold",children:"AHELM"}),", a benchmark that aggregates various datasets — including"," ",e.jsx("strong",{className:"font-bold",children:"2 new synthetic audio-text datasets"})," ","called ",e.jsx("strong",{className:"font-bold",children:"PARADE"}),", which evaluates the ALMs on avoiding stereotypes, and"," ",e.jsx("strong",{className:"font-bold",children:"CoRe-Bench"}),", which measures reasoning over conversational audio through inferential multi-turn question answering — to holistically measure the performance of ALMs across 10 aspects we have identified as important to the development and usage of ALMs:"," ",e.jsx("em",{className:"italic",children:"audio perception"}),","," ",e.jsx("em",{className:"italic",children:"knowledge"}),","," ",e.jsx("em",{className:"italic",children:"reasoning"}),","," ",e.jsx("em",{className:"italic",children:"emotion detection"}),","," ",e.jsx("em",{className:"italic",children:"bias"}),", ",e.jsx("em",{className:"italic",children:"fairness"}),","," ",e.jsx("em",{className:"italic",children:"multilinguality"}),","," ",e.jsx("em",{className:"italic",children:"robustness"}),","," ",e.jsx("em",{className:"italic",children:"toxicity"}),", and"," ",e.jsx("em",{className:"italic",children:"safety"}),". We standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models."]}),e.jsxs("div",{className:"my-16 flex flex-col lg:flex-row gap-8",children:[e.jsx("div",{className:"flex-1 text-xl",children:e.jsx("img",{src:Qt,alt:"An example of each aspect in AHELM: Auditory Perception, Knowledge, Reasoning, Emotion Detection, Bias, Fairness, Multilinguality, Robustness, Toxicity and Safety. ",className:""})}),e.jsxs("div",{className:"flex-1",children:[e.jsx(E,{}),e.jsx(b,{to:"leaderboard",className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})]})]})]})}function Kt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Arabic"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsxs("p",{className:"my-4",children:["As part of our efforts to better understand the multilingual capabilities of large language models (LLMs), we present"," ",e.jsx("strong",{className:"font-bold",children:"HELM Arabic"}),", a leaderboard for transparent and reproducible evaluation of LLMs on Arabic language benchmarks. This leaderboard was produced in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsxs("p",{className:"my-4",children:["HELM Arabic builds on a collection of established Arabic-language evaluation tasks that are widely used in the research community (",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),"). It includes the following seven benchmarks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/AlGhafa-Arabic-LLM-Benchmark-Native",children:"AlGhafa"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2023.arabicnlp-1.21/",children:"Almazrouei et al., 2023"}),") — an Arabic language multiple choice evaluation benchmark derived from publicly available NLP datasets"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/ArabicMMLU",children:"ArabicMMLU"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a native Arabic language question answering benchmark using questions sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/mhardalov/exams",children:"Arabic EXAMS"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2020.emnlp-main.438/",children:"Hardalov et al., 2020"}),") — the Arabic language subset of the EXAMS multilingual question answering benchmark, which consists of high school exam questions across various school subjects"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/MadinahQA",children:"MadinahQA"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a question answering benchmark published by MBZUAI that tests knowledge of Arabic language and grammar"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/asas-ai/AraTrust",children:"AraTrust"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"Alghamdi et al., 2025"}),") — an Arab-region-specific safety evaluation dataset consisting of human-written questions including direct attacks, indirect attacks, and harmless requests with sensitive words"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/ALRAGE",children:"ALRAGE"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),") — an Arabic language passage-based open-ended model-graded question answering benchmark that reflects retrieval-augmented generation use cases"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/human_translated_arabic_mmlu",children:"ArbMMLU-HT"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2308.16149",children:"Sengupta et al., 2023"}),") — a translation of MMLU to Arabic by human translators published by MBZUAI"]})]}),e.jsxs("p",{className:"my-4",children:["The"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm.stanford.edu/helm/arabic/latest/",children:"leaderboard results"})," ","show that LLMs have made significant progress in Arabic language understanding over the last few years. As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/",children:"reproducible"})," ","using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"HELM open source framework"}),". We hope that this leaderboard will be a valuable resource for the Arabic NLP community."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/12/18/helm-arabic.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Xt(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Arabic"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsxs("p",{className:"my-4",children:["We present"," ",e.jsx("strong",{className:"font-bold",children:"HELM Arabic Enterpise"}),", a leaderboard for transparent and reproducible evaluation of LLMs on Arabic language benchmarks for enterprise use cases. This leaderboard was created in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsx("p",{className:"my-4",children:"HELM Arabic Enterprise introduces new datasets for the following tasks:"}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("strong",{className:"font-bold",children:"Content Generation"})," — Given summaries of from real news articles, the LLM is prompted to generate new articles in a corporate style. The generated articles are then graded by an LLM-as-judge for faithfulness, completeness, and style adherence."]}),e.jsxs("li",{children:[e.jsx("strong",{className:"font-bold",children:"Financial"})," — Given questions from English language finance textbooks that have been translated to Arabic using machine translation, the LLM is prompted to answer the questions in one of the following three settings. 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Furthermore, they differ in their evaluation procedures and the scope of the evaluation, making it difficult to compare models. To address these issues, we extend the HELM framework to VLMs to present the Holistic Evaluation of Vision Language Models (VHELM). To address these issues, we introduce VHELM, built on HELM for language models. VHELM aggregates various datasets to cover one or more of the 9 aspects:"," ",e.jsx("b",{children:"visual perception"}),", ",e.jsx("b",{children:"bias"}),", ",e.jsx("b",{children:"fairness"}),", ",e.jsx("b",{children:"knowledge"}),", ",e.jsx("b",{children:"multilinguality"}),", ",e.jsx("b",{children:"reasoning"}),", ",e.jsx("b",{children:"robustness"}),","," ",e.jsx("b",{children:"safety"}),", and ",e.jsx("b",{children:"toxicity"}),". In doing so, we produce a comprehensive, multi-dimensional view of the capabilities of the VLMs across these important factors. 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We also introduce the SEA-HELM leaderboard, which allows users to understand models' multilingual and multicultural performance in a systematic and user-friendly manner."]}),e.jsxs("p",{className:"mb-4 italic",children:["Additional evaluation results are available on the external"," ",e.jsx("a",{href:"https://leaderboard.sea-lion.ai/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"AI Singapore (AISG) SEA-HELM leaderboard"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://arxiv.org/abs/2502.14301",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"HELM Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://leaderboard.sea-lion.ai/",children:"AISG Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Wa(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM Speech"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsx("p",{className:"my-4",children:"We present a HELM leaderboard for speech tasks."}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function za(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Long Context"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsx("p",{className:"my-4",children:"Recent Large Language Models (LLMs) support processing long inputs with hundreds of thousands or millions of tokens. Long context capabilities are important for many real-world applications, such as processing long text documents, conducting long conversations or following complex instructions. However, support for long inputs does not equate to strong long context capabilities. As such, there is a need for rigorous and comprehensive evaluations of long context capabilities."}),e.jsxs("p",{className:"my-4",children:["To address this, we introduce the"," ",e.jsx("strong",{className:"font-bold",children:"HELM Long Context"})," ","leaderboard, which provides transparent, comparable and reproducible evaluations of long context capabilities of recent models. The benchmark consists of 5 tasks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_squad",children:e.jsx("strong",{className:"font-bold",children:"RULER SQuAD"})})," ","— open ended single-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/ruler_hotpotqa",children:e.jsx("strong",{className:"font-bold",children:"RULER HotPotQA"})})," ","— open ended multi-hop question answering on passages"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_qa",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.MC"})})," ","— multiple choice question answering based on the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/infinite_bench_en_sum",children:e.jsx("strong",{className:"font-bold",children:"∞Bench En.Sum"})})," ","— summarization of the plot of a novel"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"#/leaderboard/openai_mrcr",children:e.jsx("strong",{className:"font-bold",children:"OpenAI MRCR"})})," ","— multi-round co-reference resolution on a long, multi-turn, synthetic conversation"]})]}),e.jsx("p",{className:"my-4",children:"The results demonstrate that even though significant progress has been made on long context capabilities, there is still considerable room for improvement."}),e.jsx("p",{className:"my-4",children:"As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are reproducible using the HELM open source framework."}),e.jsxs("p",{className:"my-4",children:["This leaderboard was produced through research collaboration with"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://www.lvmh.com/",children:"LVMH"}),", and was funded by the"," ",e.jsx("a",{className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://hai.stanford.edu/corporate-affiliate-program",children:"HAI Corporate Affiliate Program"}),"."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/09/29/helm-long-context.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Va(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-8 font-bold text-center",children:"HELM SQL"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("p",{className:"mb-4",children:["Text-to-SQL is the task of converting natural language instructions into SQL code. There has been increasing interest in text-to-SQL for applications by data scientists in various domains. Thus, we introduce the ",e.jsx("strong",{className:"font-bold",children:"HELM SQL"})," ","leaderboard for text-to-SQL evaluations. The HELM SQL leaderboard evaluates leading LLMs on two existing text-to-SQL benchmarks (Spider, BIRD-SQL) that cover a range of professional domains. In addition, we introduce a new benchmark,"," ",e.jsx("strong",{className:"font-bold",children:"CzechBankQA"}),", a text-to-SQL benchmark based on a real public bank customer relational database, to address the lack of coverage of text-to-SQL in the financial domain. CzechBankQA consists of text-to-SQL queries and gold labels provided by professionals at Wells Fargo. 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Despite being trained on large multilingual corpora, most open-source LLMs struggle with Vietnamese understanding and generation.",e.jsx("strong",{children:" ViLLM"})," addresses this gap by providing a robust evaluation framework tailored specifically for Vietnamese. It includes ",e.jsx("strong",{children:"11 essential scenarios"}),", each targeting a core capability of Vietnamese LLMs:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"ViLLM"})," includes 11 carefully designed evaluation scenarios, each addressing a core language modeling capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Question Answering:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/juletxara/xquad_xtreme",children:"XQuAD"}),","," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/facebook/mlqa",children:"MLQA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Summarization:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/Yuhthe/vietnews",children:"VietNews"}),","," 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language models:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"Bias Assessment:"})," Detects and mitigates biased patterns in model outputs."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Toxicity Assessment:"})," Monitors and controls the generation of harmful or offensive content."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Fairness Evaluation:"})," Ensures equitable performance across demographic groups and languages."]}),e.jsxs("li",{children:[e.jsx("strong",{children:"Robustness Analysis:"})," Evaluates model stability against noisy or adversarial inputs in real-world scenarios."]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://aclanthology.org/2024.findings-naacl.182",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full 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continue to evolve and transform how we interact with technology, there is a growing need for standardized evaluation frameworks that can assess their capabilities across diverse languages and tasks. Despite significant advances in speech processing, many models struggle with multilingual understanding, accent recognition, and domain-specific speech tasks."," ",e.jsx("strong",{children:"SLPHelm"})," addresses these challenges by providing a robust evaluation framework that includes",e.jsx("strong",{children:" 5 key scenarios"})," across ",e.jsx("strong",{children:"15 models"}),", each targeting essential speech processing capabilities:"]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," includes 5 carefully designed evaluation scenarios, each addressing a core speech processing capability:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Transcription Accuracy"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Type Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Symptom Diagnosis"})}),e.jsx("li",{children:e.jsx("strong",{children:"Disorder Diagnosis via Transcription"})})]})]}),e.jsxs("p",{className:"my-4",children:[e.jsx("strong",{children:"SLPHelm"})," evaluates models using comprehensive datasets:",e.jsxs("ul",{className:"list-disc list-inside mt-2",children:[e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmUltraSuite",children:"UltraSuite"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"ENNI:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/ENNI",children:"ENNI"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"LeNormand:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/LeNormand",children:"LeNormand"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"PERCEPT-GFTA:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmDataset/tree/main/PERCEPT-GFTA",children:"PERCEPT-GFTA"})]}),e.jsxs("li",{children:[e.jsx("strong",{children:"UltraSuite w/ Manual Labels:"})," ",e.jsx("a",{className:"link-primary",href:"https://huggingface.co/datasets/SAA-Lab/SLPHelmManualLabels",children:"SLPHelmManualLabels"})]})]})]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"py-2 pb-6 rounded-3xl bg-gray-100 h-full",style:{maxWidth:"100%"},children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const Qa=""+new URL("audio-table-Dn5NMMeJ.png",import.meta.url).href;function Za(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:"Holistic Evaluation of Audio-Language Models"}),e.jsxs("div",{className:"flex flex-col sm:flex-row justify-center gap-2 sm:gap-8 md:gap-32 my-8",children:[e.jsx("a",{className:"px-10 btn rounded-md",href:"https://arxiv.org/abs/2508.21376",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"#/leaderboard",children:"Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://github.com/stanford-crfm/helm",children:"Github"})]}),e.jsxs("p",{className:"my-4",children:["Evaluations of"," ",e.jsx("strong",{className:"font-bold",children:"audio-language models (ALMs)"})," ","— multimodal models that take interleaved audio and text as input and output text — are hindered by the lack of standardized benchmarks; most benchmarks measure only one or two capabilities and omit evaluative aspects such as fairness or safety. Furthermore, comparison across models is difficult as separate evaluations test a limited number of models and use different prompting methods and inference parameters."]}),e.jsxs("p",{className:"my-4",children:["To address these shortfalls, we introduce"," ",e.jsx("strong",{className:"font-bold",children:"AHELM"}),", a benchmark that aggregates various datasets — including"," ",e.jsx("strong",{className:"font-bold",children:"2 new synthetic audio-text datasets"})," ","called ",e.jsx("strong",{className:"font-bold",children:"PARADE"}),", which evaluates the ALMs on avoiding stereotypes, and"," ",e.jsx("strong",{className:"font-bold",children:"CoRe-Bench"}),", which measures reasoning over conversational audio through inferential multi-turn question answering — to holistically measure the performance of ALMs across 10 aspects we have identified as important to the development and usage of ALMs:"," ",e.jsx("em",{className:"italic",children:"audio perception"}),","," ",e.jsx("em",{className:"italic",children:"knowledge"}),","," ",e.jsx("em",{className:"italic",children:"reasoning"}),","," ",e.jsx("em",{className:"italic",children:"emotion detection"}),","," ",e.jsx("em",{className:"italic",children:"bias"}),", ",e.jsx("em",{className:"italic",children:"fairness"}),","," ",e.jsx("em",{className:"italic",children:"multilinguality"}),","," ",e.jsx("em",{className:"italic",children:"robustness"}),","," ",e.jsx("em",{className:"italic",children:"toxicity"}),", and"," ",e.jsx("em",{className:"italic",children:"safety"}),". We standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models."]}),e.jsxs("div",{className:"my-16 flex flex-col lg:flex-row gap-8",children:[e.jsx("div",{className:"flex-1 text-xl",children:e.jsx("img",{src:Qa,alt:"An example of each aspect in AHELM: Auditory Perception, Knowledge, Reasoning, Emotion Detection, Bias, Fairness, Multilinguality, Robustness, Toxicity and Safety. 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This leaderboard was produced in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsxs("p",{className:"my-4",children:["HELM Arabic builds on a collection of established Arabic-language evaluation tasks that are widely used in the research community (",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),"). It includes the following seven benchmarks:"]}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/AlGhafa-Arabic-LLM-Benchmark-Native",children:"AlGhafa"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2023.arabicnlp-1.21/",children:"Almazrouei et al., 2023"}),") — an Arabic language multiple choice evaluation benchmark derived from publicly available NLP datasets"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/ArabicMMLU",children:"ArabicMMLU"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a native Arabic language question answering benchmark using questions sourced from school exams across diverse educational levels in different countries spanning North Africa, the Levant, and the Gulf regions"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/mhardalov/exams",children:"Arabic EXAMS"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2020.emnlp-main.438/",children:"Hardalov et al., 2020"}),") — the Arabic language subset of the EXAMS multilingual question answering benchmark, which consists of high school exam questions across various school subjects"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/MadinahQA",children:"MadinahQA"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://aclanthology.org/2024.findings-acl.334/",children:"Koto et al., 2024"}),") — a question answering benchmark published by MBZUAI that tests knowledge of Arabic language and grammar"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/asas-ai/AraTrust",children:"AraTrust"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"Alghamdi et al., 2025"}),") — an Arab-region-specific safety evaluation dataset consisting of human-written questions including direct attacks, indirect attacks, and harmless requests with sensitive words"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/OALL/ALRAGE",children:"ALRAGE"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/blog/leaderboard-arabic-v2",children:"El Filali et al., 2025"}),") — an Arabic language passage-based open-ended model-graded question answering benchmark that reflects retrieval-augmented generation use cases"]}),e.jsxs("li",{children:[e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://huggingface.co/datasets/MBZUAI/human_translated_arabic_mmlu",children:"ArbMMLU-HT"})," ","(",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2308.16149",children:"Sengupta et al., 2023"}),") — a translation of MMLU to Arabic by human translators published by MBZUAI"]})]}),e.jsxs("p",{className:"my-4",children:["The"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm.stanford.edu/helm/arabic/latest/",children:"leaderboard results"})," ","show that LLMs have made significant progress in Arabic language understanding over the last few years. As with all HELM leaderboards, this leaderboard provides full transparency into all LLM requests and responses, and the results are"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://crfm-helm.readthedocs.io/en/latest/reproducing_leaderboards/",children:"reproducible"})," ","using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"HELM open source framework"}),". We hope that this leaderboard will be a valuable resource for the Arabic NLP community."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2025/12/18/helm-arabic.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}function Xa(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsx("h1",{className:"text-3xl my-4 font-bold text-center",children:"HELM Arabic Enterprise"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-[1] text-l",children:[e.jsxs("p",{className:"my-4",children:["We present HELM Arabic Enterprise, a leaderboard for transparent, reproducible evaluation of large language models on Arabic-language benchmarks designed around enterprise use cases. The leaderboard was developed in collaboration with"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arabic.ai/",children:"Arabic.AI"}),"."]}),e.jsx("p",{className:"my-4",children:"Arabic enterprise applications often require more than general conversational ability. Models must generate grounded content, reason over financial concepts, answer domain-specific legal questions, and operate reliably in Arabic across formal, professional, and institutional registers. HELM Arabic Enterprise evaluates these capabilities through six tasks across content generation, financial reasoning, and legal question answering:"}),e.jsxs("ul",{className:"list-disc pl-6",children:[e.jsx("li",{children:"Article Generation"}),e.jsx("li",{children:"Financial Multiple Choice Question Anwering"}),e.jsx("li",{children:"Financial Boolean Verification"}),e.jsx("li",{children:"Financial Calculation"}),e.jsx("li",{children:"Legal Open-book Question Answering"}),e.jsx("li",{children:"Legal Closed-book Question Answering"})]}),e.jsxs("p",{className:"my-4",children:["As with all HELM leaderboards, HELM Arabic Enterprise emphasizes transparency and reproducibility. All model requests, responses, prompts, metrics, and scores are made available for inspection. Results can be reproduced using the"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://github.com/stanford-crfm/helm/",children:"open-source HELM framework"}),", allowing researchers and practitioners to audit model behavior rather than relying only on aggregate scores. We hope HELM Arabic Enterprise becomes a useful resource for the Arabic NLP community and for organizations evaluating LLMs for Arabic enterprise applications."]}),e.jsxs("div",{className:"flex flex-row justify-center mt-4",children:[e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"https://crfm.stanford.edu/2026/05/26/helm-arabic-enterprise.html",children:"Blog Post"}),e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})]})]}),e.jsxs("div",{className:"flex-[1] py-2 rounded-3xl bg-gray-100 h-full",children:[e.jsx(E,{}),e.jsx("div",{className:"flex justify-end",children:e.jsx(b,{to:"leaderboard",children:e.jsx("button",{className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})})})]})]})]})}const Ya=""+new URL("overview-CVXNopt8.png",import.meta.url).href;function et(){return e.jsxs("div",{className:"container mx-auto px-16",children:[e.jsxs("h1",{className:"text-3xl mt-16 my-8 font-bold text-center",children:[e.jsx("strong",{children:"RoboReward"}),": General-purpose Vision-Language Reward Models for Robotics"]}),e.jsxs("div",{className:"flex flex-col sm:flex-row justify-center gap-2 sm:gap-4 md:gap-8 my-8",children:[e.jsx("a",{className:"px-10 btn rounded-md",href:"https://arxiv.org/abs/2601.00675",children:"Paper"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://crfm.stanford.edu/helm/robo-reward-bench/latest/#/leaderboard",children:"Full Leaderboard"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/datasets/teetone/RoboReward",children:"RoboReward Dataset"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/teetone/RoboReward-4B",children:"RoboReward 4B"}),e.jsx("a",{className:"px-10 btn rounded-md",href:"https://huggingface.co/teetone/RoboReward-8B",children:"RoboReward 8B"})]}),e.jsx("p",{className:"my-4",children:"A well-designed reward is critical for effective reinforcement learning-based policy improvement. 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Because OXE is success-heavy and lacks failure examples, we propose a"," ",e.jsx("em",{children:"negative examples data augmentation"})," pipeline that generates calibrated ",e.jsx("em",{children:"negatives"})," and ",e.jsx("em",{children:"near-misses"})," via counterfactual relabeling of successful episodes and temporal clipping to create partial-progress outcomes from the same videos."]}),e.jsx("p",{className:"my-4",children:"Using this framework, we produce an extensive training and evaluation dataset that spans diverse tasks and embodiments and enables systematic evaluation of whether state-of-the-art VLMs can reliably provide rewards for robotics. Our evaluation of leading open-weight and proprietary VLMs reveals that no model excels across all tasks, underscoring substantial room for improvement. We then train general-purpose 4B- and 8B-parameter models that outperform much larger VLMs in assigning rewards for short-horizon robotic tasks. Finally, we deploy the 8B reward VLM in real-robot reinforcement learning and find that it improves policy learning over Gemini Robotics-ER 1.5, a frontier physical reasoning VLM trained on robotics data, by a large margin, while substantially narrowing the gap to RL training with human-provided rewards."}),e.jsxs("div",{className:"my-12 flex flex-col lg:flex-row gap-8 items-start",children:[e.jsx("div",{className:"flex-1",children:e.jsx("img",{src:Ya,alt:"RoboReward overview",className:"w-full max-w-full rounded-lg shadow"})}),e.jsxs("div",{className:"flex-1",children:[e.jsx(E,{}),e.jsx(b,{to:"leaderboard",className:"px-4 mx-3 mt-3 btn bg-white rounded-md",children:e.jsx("span",{children:"See more"})})]})]})]})}function st(){return window.PROJECT_ID==="lite"?e.jsx(ge,{}):window.PROJECT_ID==="instruct"?e.jsx(ha,{}):window.PROJECT_ID==="image2struct"?e.jsx(Ta,{}):window.PROJECT_ID==="heim"?e.jsx(va,{}):window.PROJECT_ID==="mmlu"?e.jsx(ua,{}):window.PROJECT_ID==="vhelm"?e.jsx(La,{}):window.PROJECT_ID==="air-bench"?e.jsx(fa,{}):window.PROJECT_ID==="thaiexam"?e.jsx(pa,{}):window.PROJECT_ID==="finance"?e.jsx(ga,{}):window.PROJECT_ID==="cleva"?e.jsx(Ma,{}):window.PROJECT_ID==="torr"?e.jsx(Ea,{}):window.PROJECT_ID==="ewok"?e.jsx(Pa,{}):window.PROJECT_ID==="medhelm"&&window.RELEASE=="v1.0.0"?e.jsx(Da,{}):window.PROJECT_ID==="medhelm"?e.jsx(Ha,{}):window.PROJECT_ID==="safety"?e.jsx(Oa,{}):window.PROJECT_ID==="capabilities"?e.jsx(Fa,{}):window.PROJECT_ID==="mmlu-winogrande-afr"?e.jsx(Ua,{}):window.PROJECT_ID==="seahelm"?e.jsx(qa,{}):window.PROJECT_ID==="speech"?e.jsx(Wa,{}):window.PROJECT_ID==="sql"?e.jsx(Va,{}):window.PROJECT_ID==="long-context"?e.jsx(za,{}):window.PROJECT_ID==="villm"?e.jsx(Ja,{}):window.PROJECT_ID==="slphelm"?e.jsx(Ga,{}):window.PROJECT_ID==="audio"?e.jsx(Za,{}):window.PROJECT_ID==="arabic"?e.jsx(Ka,{}):window.PROJECT_ID==="robo-reward-bench"?e.jsx(et,{}):window.PROJECT_ID==="arabic-enterprise"?e.jsx(Xa,{}):window.PROJECT_ID==="home"?e.jsx(Ra,{}):e.jsx(ge,{})}function at(){return e.jsx(qe,{children:e.jsx(We,{children:e.jsxs(I,{path:"/",element:e.jsx(fs,{}),children:[e.jsx(I,{index:!0,element:e.jsx(st,{})}),e.jsx(I,{path:"leaderboard",element:e.jsx(ae,{})}),e.jsx(I,{path:"leaderboard/:groupName",element:e.jsx(ae,{})}),e.jsx(I,{path:"models",element:e.jsx(vs,{})}),e.jsx(I,{path:"scenarios",element:e.jsx(ws,{})}),e.jsx(I,{path:"groups",element:e.jsx(Ms,{})}),e.jsx(I,{path:"groups/:groupName",element:e.jsx(ae,{})}),e.jsx(I,{path:"runs",element:e.jsx(Ss,{})}),e.jsx(I,{path:"runs/:runName",element:e.jsx(ia,{})})]})})})}Qe.createRoot(document.getElementById("root")).render(e.jsx(ze.StrictMode,{children:e.jsx(at,{})}));
diff --git a/src/helm/benchmark/static_build/assets/react-BhnNyHuP.js b/src/helm/benchmark/static_build/assets/react-D3IQTVQZ.js
similarity index 78%
rename from src/helm/benchmark/static_build/assets/react-BhnNyHuP.js
rename to src/helm/benchmark/static_build/assets/react-D3IQTVQZ.js
index 10f72f80f4b..3acee4f72bd 100644
--- a/src/helm/benchmark/static_build/assets/react-BhnNyHuP.js
+++ b/src/helm/benchmark/static_build/assets/react-D3IQTVQZ.js
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i.getSnapshotBeforeUpdate=="function"&&(t.flags|=1024)):(typeof i.componentDidUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=4),typeof i.getSnapshotBeforeUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=F),i.props=r,i.state=F,i.context=f,r=y):(typeof i.componentDidUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=4),typeof i.getSnapshotBeforeUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=1024),r=!1)}return ii(e,t,n,r,u,l)}function ii(e,t,n,r,l,u){rs(e,t);var i=(t.flags&128)!==0;if(!r&&!i)return l&&sa(t,n,!1),_t(e,t,u);r=t.stateNode,Bf.current=t;var s=i&&typeof n.getDerivedStateFromError!="function"?null:r.render();return t.flags|=1,e!==null&&i?(t.child=zn(t,e.child,null,u),t.child=zn(t,null,s,u)):Ie(e,t,s,u),t.memoizedState=r.state,l&&sa(t,n,!0),t.child}function us(e){var t=e.stateNode;t.pendingContext?oa(e,t.pendingContext,t.pendingContext!==t.context):t.context&&oa(e,t.context,!1),Hu(e,t.containerInfo)}function is(e,t,n,r,l){return Rn(),Fu(l),t.flags|=256,Ie(e,t,n,r),t.child}var oi={dehydrated:null,treeContext:null,retryLane:0};function ai(e){return{baseLanes:e,cachePool:null,transitions:null}}function os(e,t,n){var r=t.pendingProps,l=pe.current,u=!1,i=(t.flags&128)!==0,s;if((s=i)||(s=e!==null&&e.memoizedState===null?!1:(l&2)!==0),s?(u=!0,t.flags&=-129):(e===null||e.memoizedState!==null)&&(l|=1),ae(pe,l&1),e===null)return 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f={mode:"hidden",children:r.children};return(i&1)===0&&t.child!==l?(r=t.child,r.childLanes=0,r.pendingProps=f,t.deletions=null):(r=Kt(l,f),r.subtreeFlags=l.subtreeFlags&14680064),s!==null?u=Kt(s,u):(u=cn(u,i,n,null),u.flags|=2),u.return=t,r.return=t,r.sibling=u,t.child=r,r=u,u=t.child,i=e.child.memoizedState,i=i===null?ai(n):{baseLanes:i.baseLanes|n,cachePool:null,transitions:i.transitions},u.memoizedState=i,u.childLanes=e.childLanes&~n,t.memoizedState=oi,r}return u=e.child,e=u.sibling,r=Kt(u,{mode:"visible",children:r.children}),(t.mode&1)===0&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function si(e,t){return t=Ll({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function yl(e,t,n,r){return r!==null&&Fu(r),zn(t,e.child,null,n),e=si(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function Af(e,t,n,r,l,u,i){if(n)return t.flags&256?(t.flags&=-257,r=ri(Error(a(422))),yl(e,t,i,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(u=r.fallback,l=t.mode,r=Ll({mode:"visible",children:r.children},l,0,null),u=cn(u,l,i,null),u.flags|=2,r.return=t,u.return=t,r.sibling=u,t.child=r,(t.mode&1)!==0&&zn(t,e.child,null,i),t.child.memoizedState=ai(i),t.memoizedState=oi,u);if((t.mode&1)===0)return yl(e,t,i,null);if(l.data==="$!"){if(r=l.nextSibling&&l.nextSibling.dataset,r)var s=r.dgst;return r=s,u=Error(a(419)),r=ri(u,r,void 0),yl(e,t,i,r)}if(s=(i&e.childLanes)!==0,Ae||s){if(r=_e,r!==null){switch(i&-i){case 4:l=2;break;case 16:l=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:l=32;break;case 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r=t.pendingProps,l=r.revealOrder,u=r.tail;if(Ie(e,t,r.children,n),r=pe.current,(r&2)!==0)r=r&1|2,t.flags|=128;else{if(e!==null&&(e.flags&128)!==0)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&as(e,n,t);else if(e.tag===19)as(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(ae(pe,r),(t.mode&1)===0)t.memoizedState=null;else switch(l){case"forwards":for(n=t.child,l=null;n!==null;)e=n.alternate,e!==null&&cl(e)===null&&(l=n),n=n.sibling;n=l,n===null?(l=t.child,t.child=null):(l=n.sibling,n.sibling=null),ci(t,!1,l,n,u);break;case"backwards":for(n=null,l=t.child,t.child=null;l!==null;){if(e=l.alternate,e!==null&&cl(e)===null){t.child=l;break}e=l.sibling,l.sibling=n,n=l,l=e}ci(t,!0,n,null,u);break;case"together":ci(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function 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y=u;(t.updateQueue=y)&&(t.flags|=4)}},ds=function(e,t,n,r){n!==r&&(t.flags|=4)};function yr(e,t){if(!de)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;t!==null;)t.alternate!==null&&(n=t),t=t.sibling;n===null?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;n!==null;)n.alternate!==null&&(r=n),n=n.sibling;r===null?t||e.tail===null?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Te(e){var t=e.alternate!==null&&e.alternate.child===e.child,n=0,r=0;if(t)for(var l=e.child;l!==null;)n|=l.lanes|l.childLanes,r|=l.subtreeFlags&14680064,r|=l.flags&14680064,l.return=e,l=l.sibling;else for(l=e.child;l!==null;)n|=l.lanes|l.childLanes,r|=l.subtreeFlags,r|=l.flags,l.return=e,l=l.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Wf(e,t,n){var r=t.pendingProps;switch(Mu(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Te(t),null;case 1:return Be(t.type)&&br(),Te(t),null;case 3:return 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u=t.memoizedProps;switch(r[ht]=t,r[or]=u,e=(t.mode&1)!==0,n){case"dialog":se("cancel",r),se("close",r);break;case"iframe":case"object":case"embed":se("load",r);break;case"video":case"audio":for(l=0;l<lr.length;l++)se(lr[l],r);break;case"source":se("error",r);break;case"img":case"image":case"link":se("error",r),se("load",r);break;case"details":se("toggle",r);break;case"input":Qi(r,u),se("invalid",r);break;case"select":r._wrapperState={wasMultiple:!!u.multiple},se("invalid",r);break;case"textarea":Xi(r,u),se("invalid",r)}Ql(n,u),l=null;for(var i in u)if(u.hasOwnProperty(i)){var s=u[i];i==="children"?typeof s=="string"?r.textContent!==s&&(u.suppressHydrationWarning!==!0&&Zr(r.textContent,s,e),l=["children",s]):typeof 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r.is=="string"?e=i.createElement(n,{is:r.is}):(e=i.createElement(n),n==="select"&&(i=e,r.multiple?i.multiple=!0:r.size&&(i.size=r.size))):e=i.createElementNS(e,n),e[ht]=t,e[or]=r,cs(e,t,!1,!1),t.stateNode=e;e:{switch(i=Kl(n,r),n){case"dialog":se("cancel",e),se("close",e),l=r;break;case"iframe":case"object":case"embed":se("load",e),l=r;break;case"video":case"audio":for(l=0;l<lr.length;l++)se(lr[l],e);l=r;break;case"source":se("error",e),l=r;break;case"img":case"image":case"link":se("error",e),se("load",e),l=r;break;case"details":se("toggle",e),l=r;break;case"input":Qi(e,r),l=Al(e,r),se("invalid",e);break;case"option":l=r;break;case"select":e._wrapperState={wasMultiple:!!r.multiple},l=D({},r,{value:void 0}),se("invalid",e);break;case"textarea":Xi(e,r),l=Hl(e,r),se("invalid",e);break;default:l=r}Ql(n,l),s=l;for(u in s)if(s.hasOwnProperty(u)){var f=s[u];u==="style"?eo(e,f):u==="dangerouslySetInnerHTML"?(f=f?f.__html:void 0,f!=null&&qi(e,f)):u==="children"?typeof 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i.getSnapshotBeforeUpdate=="function"&&(t.flags|=1024)):(typeof i.componentDidUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=4),typeof i.getSnapshotBeforeUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=F),i.props=r,i.state=F,i.context=f,r=y):(typeof i.componentDidUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=4),typeof i.getSnapshotBeforeUpdate!="function"||s===e.memoizedProps&&k===e.memoizedState||(t.flags|=1024),r=!1)}return ii(e,t,n,r,u,l)}function ii(e,t,n,r,l,u){rs(e,t);var i=(t.flags&128)!==0;if(!r&&!i)return l&&sa(t,n,!1),_t(e,t,u);r=t.stateNode,Af.current=t;var s=i&&typeof n.getDerivedStateFromError!="function"?null:r.render();return t.flags|=1,e!==null&&i?(t.child=zn(t,e.child,null,u),t.child=zn(t,null,s,u)):Ie(e,t,s,u),t.memoizedState=r.state,l&&sa(t,n,!0),t.child}function us(e){var t=e.stateNode;t.pendingContext?oa(e,t.pendingContext,t.pendingContext!==t.context):t.context&&oa(e,t.context,!1),Hu(e,t.containerInfo)}function is(e,t,n,r,l){return Rn(),Fu(l),t.flags|=256,Ie(e,t,n,r),t.child}var oi={dehydrated:null,treeContext:null,retryLane:0};function ai(e){return{baseLanes:e,cachePool:null,transitions:null}}function os(e,t,n){var r=t.pendingProps,l=pe.current,u=!1,i=(t.flags&128)!==0,s;if((s=i)||(s=e!==null&&e.memoizedState===null?!1:(l&2)!==0),s?(u=!0,t.flags&=-129):(e===null||e.memoizedState!==null)&&(l|=1),ae(pe,l&1),e===null)return 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f={mode:"hidden",children:r.children};return(i&1)===0&&t.child!==l?(r=t.child,r.childLanes=0,r.pendingProps=f,t.deletions=null):(r=Kt(l,f),r.subtreeFlags=l.subtreeFlags&14680064),s!==null?u=Kt(s,u):(u=cn(u,i,n,null),u.flags|=2),u.return=t,r.return=t,r.sibling=u,t.child=r,r=u,u=t.child,i=e.child.memoizedState,i=i===null?ai(n):{baseLanes:i.baseLanes|n,cachePool:null,transitions:i.transitions},u.memoizedState=i,u.childLanes=e.childLanes&~n,t.memoizedState=oi,r}return u=e.child,e=u.sibling,r=Kt(u,{mode:"visible",children:r.children}),(t.mode&1)===0&&(r.lanes=n),r.return=t,r.sibling=null,e!==null&&(n=t.deletions,n===null?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=r,t.memoizedState=null,r}function si(e,t){return t=Ll({mode:"visible",children:t},e.mode,0,null),t.return=e,e.child=t}function yl(e,t,n,r){return r!==null&&Fu(r),zn(t,e.child,null,n),e=si(t,t.pendingProps.children),e.flags|=2,t.memoizedState=null,e}function Vf(e,t,n,r,l,u,i){if(n)return t.flags&256?(t.flags&=-257,r=ri(Error(a(422))),yl(e,t,i,r)):t.memoizedState!==null?(t.child=e.child,t.flags|=128,null):(u=r.fallback,l=t.mode,r=Ll({mode:"visible",children:r.children},l,0,null),u=cn(u,l,i,null),u.flags|=2,r.return=t,u.return=t,r.sibling=u,t.child=r,(t.mode&1)!==0&&zn(t,e.child,null,i),t.child.memoizedState=ai(i),t.memoizedState=oi,u);if((t.mode&1)===0)return yl(e,t,i,null);if(l.data==="$!"){if(r=l.nextSibling&&l.nextSibling.dataset,r)var s=r.dgst;return r=s,u=Error(a(419)),r=ri(u,r,void 0),yl(e,t,i,r)}if(s=(i&e.childLanes)!==0,Ae||s){if(r=_e,r!==null){switch(i&-i){case 4:l=2;break;case 16:l=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:l=32;break;case 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r=t.pendingProps,l=r.revealOrder,u=r.tail;if(Ie(e,t,r.children,n),r=pe.current,(r&2)!==0)r=r&1|2,t.flags|=128;else{if(e!==null&&(e.flags&128)!==0)e:for(e=t.child;e!==null;){if(e.tag===13)e.memoizedState!==null&&as(e,n,t);else if(e.tag===19)as(e,n,t);else if(e.child!==null){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;e.sibling===null;){if(e.return===null||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(ae(pe,r),(t.mode&1)===0)t.memoizedState=null;else switch(l){case"forwards":for(n=t.child,l=null;n!==null;)e=n.alternate,e!==null&&cl(e)===null&&(l=n),n=n.sibling;n=l,n===null?(l=t.child,t.child=null):(l=n.sibling,n.sibling=null),ci(t,!1,l,n,u);break;case"backwards":for(n=null,l=t.child,t.child=null;l!==null;){if(e=l.alternate,e!==null&&cl(e)===null){t.child=l;break}e=l.sibling,l.sibling=n,n=l,l=e}ci(t,!0,n,null,u);break;case"together":ci(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function 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Alternatively you may provide the full path as ")+'a string in <Link to="..."> and the router will parse it for you.'}function Vd(o){return o.filter((c,a)=>a===0||c.route.path&&c.route.path.length>0)}function rc(o,c){let a=Vd(o);return c?a.map((p,g)=>g===a.length-1?p.pathname:p.pathnameBase):a.map(p=>p.pathnameBase)}function lc(o,c,a,p){p===void 0&&(p=!1);let g;typeof o=="string"?g=fn(o):(g=Cr({},o),Se(!g.pathname||!g.pathname.includes("?"),Ui("?","pathname","search",g)),Se(!g.pathname||!g.pathname.includes("#"),Ui("#","pathname","hash",g)),Se(!g.search||!g.search.includes("#"),Ui("#","search","hash",g)));let w=o===""||g.pathname==="",S=w?"/":g.pathname,N;if(S==null)N=a;else{let R=c.length-1;if(!p&&S.startsWith("..")){let W=S.split("/");for(;W[0]==="..";)W.shift(),R-=1;g.pathname=W.join("/")}N=R>=0?c[R]:"/"}let _=Ad(g,N),z=S&&S!=="/"&&S.endsWith("/"),B=(w||S===".")&&a.endsWith("/");return!_.pathname.endsWith("/")&&(z||B)&&(_.pathname+="/"),_}const uc=o=>o.replace(/\/\/+/g,"/"),Gt=o=>uc(o.join("/")),Wd=o=>o.replace(/\/+$/,"").replace(/^\/*/,"/"),Hd=o=>!o||o==="?"?"":o.startsWith("?")?o:"?"+o,$d=o=>!o||o==="#"?"":o.startsWith("#")?o:"#"+o;function Qd(o){return o!=null&&typeof o.status=="number"&&typeof o.statusText=="string"&&typeof o.internal=="boolean"&&"data"in o}const ic=["post","put","patch","delete"];new Set(ic);const Kd=["get",...ic];new Set(Kd);/**
+ * React Router v6.30.4
  *
  * Copyright (c) Remix Software Inc.
  *
@@ -47,8 +47,8 @@ Error generating stack: `+u.message+`
  * LICENSE.md file in the root directory of this source tree.
  *
  * @license MIT
- */function xr(){return xr=Object.assign?Object.assign.bind():function(o){for(var c=1;c<arguments.length;c++){var a=arguments[c];for(var p in a)Object.prototype.hasOwnProperty.call(a,p)&&(o[p]=a[p])}return o},xr.apply(this,arguments)}const Hi=M.createContext(null),Kd=M.createContext(null),dn=M.createContext(null),Bl=M.createContext(null),Nt=M.createContext({outlet:null,matches:[],isDataRoute:!1}),ic=M.createContext(null);function Yd(o,c){let{relative:a}=c===void 0?{}:c;_r()||Se(!1);let{basename:p,navigator:g}=M.useContext(dn),{hash:w,pathname:S,search:N}=sc(o,{relative:a}),x=S;return p!=="/"&&(x=S==="/"?p:Gt([p,S])),g.createHref({pathname:x,search:N,hash:w})}function _r(){return M.useContext(Bl)!=null}function Pr(){return _r()||Se(!1),M.useContext(Bl).location}function oc(o){M.useContext(dn).static||M.useLayoutEffect(o)}function ac(){let{isDataRoute:o}=M.useContext(Nt);return o?ap():Xd()}function Xd(){_r()||Se(!1);let 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Np(o){let{children:c,location:a}=o;return Jd(Bi(c),a)}new Promise(()=>{});function Bi(o,c){c===void 0&&(c=[]);let a=[];return M.Children.forEach(o,(p,g)=>{if(!M.isValidElement(p))return;let w=[...c,g];if(p.type===M.Fragment){a.push.apply(a,Bi(p.props.children,w));return}p.type!==fp&&Se(!1),!p.props.index||!p.props.children||Se(!1);let S={id:p.props.id||w.join("-"),caseSensitive:p.props.caseSensitive,element:p.props.element,Component:p.props.Component,index:p.props.index,path:p.props.path,loader:p.props.loader,action:p.props.action,errorElement:p.props.errorElement,ErrorBoundary:p.props.ErrorBoundary,hasErrorBoundary:p.props.ErrorBoundary!=null||p.props.errorElement!=null,shouldRevalidate:p.props.shouldRevalidate,handle:p.props.handle,lazy:p.props.lazy};p.props.children&&(S.children=Bi(p.props.children,w)),a.push(S)}),a}/**
- * React Router DOM v6.30.3
+ */function xr(){return xr=Object.assign?Object.assign.bind():function(o){for(var c=1;c<arguments.length;c++){var a=arguments[c];for(var p in a)({}).hasOwnProperty.call(a,p)&&(o[p]=a[p])}return o},xr.apply(null,arguments)}const Hi=M.createContext(null),Yd=M.createContext(null),dn=M.createContext(null),Bl=M.createContext(null),Nt=M.createContext({outlet:null,matches:[],isDataRoute:!1}),oc=M.createContext(null);function Xd(o,c){let{relative:a}=c===void 0?{}:c;_r()||Se(!1);let{basename:p,navigator:g}=M.useContext(dn),{hash:w,pathname:S,search:N}=cc(o,{relative:a}),_=S;return p!=="/"&&(_=S==="/"?p:Gt([p,S])),g.createHref({pathname:_,search:N,hash:w})}function _r(){return M.useContext(Bl)!=null}function Pr(){return _r()||Se(!1),M.useContext(Bl).location}function ac(o){M.useContext(dn).static||M.useLayoutEffect(o)}function sc(){let{isDataRoute:o}=M.useContext(Nt);return o?sp():Gd()}function Gd(){_r()||Se(!1);let 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diff --git a/src/helm/benchmark/static_build/assets/tremor-DyW3D1Ox.js b/src/helm/benchmark/static_build/assets/tremor-B1ftEuaD.js
similarity index 99%
rename from src/helm/benchmark/static_build/assets/tremor-DyW3D1Ox.js
rename to src/helm/benchmark/static_build/assets/tremor-B1ftEuaD.js
index 62f01195fb5..fbbfb889375 100644
--- a/src/helm/benchmark/static_build/assets/tremor-DyW3D1Ox.js
+++ b/src/helm/benchmark/static_build/assets/tremor-B1ftEuaD.js
@@ -1,4 +1,4 @@
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+import{a as Cd,r as f,R as c,b as Re,t as rn}from"./react-D3IQTVQZ.js";import{R as qt,A as Ui,C as mo,X as Bt,L as at,Y as Wt,T as pn,a as po,b as qi,D as nn,c as Gn,B as Zi,d as Qi,P as Sd,e as $d,S as Nd,f as Ji,g as Od,Z as Td,h as Md}from"./recharts-DhnSad7c.js";var ta={exports:{}},Hr={};/**
  * @license React
  * react-jsx-runtime.production.min.js
  *
diff --git a/src/helm/benchmark/static_build/index.html b/src/helm/benchmark/static_build/index.html
index 60b86e2e2ea..a7ea60e5945 100644
--- a/src/helm/benchmark/static_build/index.html
+++ b/src/helm/benchmark/static_build/index.html
@@ -7,11 +7,11 @@
     <title>Holistic Evaluation of Language Models (HELM)</title>
     <meta name="description" content="The Holistic Evaluation of Language Models (HELM) serves as a living benchmark for transparency in language models. Providing broad coverage and recognizing incompleteness, multi-metric measurements, and standardization. All data and analysis are freely accessible on the website for exploration and study." />
     <script type="text/javascript" src="./config.js"></script>
-    <script type="module" crossorigin src="./assets/index-6sfOSsz5.js"></script>
-    <link rel="modulepreload" crossorigin href="./assets/react-BhnNyHuP.js">
-    <link rel="modulepreload" crossorigin href="./assets/recharts-Bmm96ixf.js">
-    <link rel="modulepreload" crossorigin href="./assets/tremor-DyW3D1Ox.js">
-    <link rel="modulepreload" crossorigin href="./assets/react-markdown-kIsf0iIR.js">
+    <script type="module" crossorigin src="./assets/index-C3aFzfEs.js"></script>
+    <link rel="modulepreload" crossorigin href="./assets/react-D3IQTVQZ.js">
+    <link rel="modulepreload" crossorigin href="./assets/recharts-DhnSad7c.js">
+    <link rel="modulepreload" crossorigin href="./assets/tremor-B1ftEuaD.js">
+    <link rel="modulepreload" crossorigin href="./assets/react-markdown-DTLembof.js">
     <link rel="stylesheet" crossorigin href="./assets/index-Dh3duhNA.css">
   </head>
   <body class="block">