diff --git a/src/helm/benchmark/static_build/assets/index-b6ad1ddb.js b/src/helm/benchmark/static_build/assets/index-f2118805.js
similarity index 80%
rename from src/helm/benchmark/static_build/assets/index-b6ad1ddb.js
rename to src/helm/benchmark/static_build/assets/index-f2118805.js
index e8dc550860..a708f32582 100644
--- a/src/helm/benchmark/static_build/assets/index-b6ad1ddb.js
+++ b/src/helm/benchmark/static_build/assets/index-f2118805.js
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Unlike previous evaluations that focused on alignment and quality, HEIM significantly improves coverage by evaluating all models across all aspects. Our results reveal that no single model excels in all aspects, with different models demonstrating strengths in different aspects."}),e.jsx("p",{className:"my-2",children:"For full transparency, this website contains all the prompts, generated images and the results for the automated and human evaluation metrics."}),e.jsx("p",{className:"my-2",children:"Inspired by HELM, we decompose the model evaluation into four key components: aspect, scenario, adaptation, and metric:"}),e.jsx("div",{className:"container max-w-screen-lg mx-auto my-8",children:e.jsx("img",{src:"https://crfm.stanford.edu/heim/latest/images/heim-main.png",alt:"HEIM scenarios, prompts, images and metrics"})}),s&&n?e.jsxs("div",{className:"grid grid-cols-1 md:grid-cols-3 gap-8",children:[e.jsx(fe,{models:s.models}),e.jsx(ge,{runGroups:s.run_groups}),e.jsx(hn,{metricFieldMap:n,metricGroups:s.metric_groups})]}):null]})}const un=""+new URL("vhelm-framework-cde7618a.png",import.meta.url).href,fn=""+new URL("vhelm-model-6d812526.png",import.meta.url).href;function gn(){const[s,t]=l.useState(void 0);return l.useEffect(()=>{const n=new AbortController;async function a(){const r=await L(n.signal);t(r)}return a(),()=>n.abort()},[]),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:"The First Steps to Holistic Evaluation of Vision-Language Models"}),e.jsxs("p",{className:"my-4",children:["To better understand VLMs, we introduce the first version of"," ",e.jsx("em",{children:"Holistic Evaluation of Vision-Language Models (VHELM)"})," by extending the ",e.jsx("a",{href:"https://arxiv.org/abs/2211.09110",children:"HELM"})," ","framework with the necessary adaptation methods to assess the performance of 6 prominent VLMs on 3 standard VLM benchmarks."]}),e.jsx("p",{className:"my-4 font-bold",children:"This is ongoing work to achieve holistic evaluation for vision-language models, so please stay tuned!"}),e.jsxs("div",{className:"my-16 flex flex-col lg:flex-row items-center gap-8",children:[e.jsxs("div",{className:"flex-1 text-xl",children:[e.jsx("img",{src:un,alt:"An image of a helm and the text 'This helm is a' is sent to a Vision-Language Model, which produces the text 'wheel for steering a ship...'",className:""}),e.jsx("img",{src:fn,alt:"An example of an evaluation for an Aspect (Knowledge) - a Scenario (MMMU) undergoes Adaptation (multimodal multiple choice) for a Model (GPT-4 Vision), then Metrics (Exact match) are computed",className:""})]}),e.jsxs("div",{className:"flex-1",children:[e.jsx(E,{}),e.jsx(f,{to:"leaderboard",className:"px-4 mx-3 mt-1 btn bg-white rounded-md",children:e.jsx("span",{children:"See More"})})]})]}),s===void 0?null:e.jsxs("div",{className:"grid grid-cols-1 sm:grid-cols-2 gap-8",children:[e.jsx(fe,{models:s.models}),e.jsx(ge,{runGroups:s.run_groups})]})]})}const pn=""+new URL("accenture-6f97eeda.png",import.meta.url).href,jn=""+new URL("cresta-9e22b983.png",import.meta.url).href;function bn(){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 Call Center"}),e.jsxs("div",{className:"flex flex-col lg:flex-row gap-8",children:[e.jsxs("div",{className:"flex-1 text-l",children:[e.jsxs("div",{className:"text-center",children:[e.jsx("a",{href:"https://www.accenture.com/",children:e.jsx("img",{src:pn,alt:"Logo",className:"inline h-12 mx-4 my-4"})}),e.jsx("a",{href:"https://www.cresta.com/",children:e.jsx("img",{src:jn,alt:"Logo",className:"inline h-8 mx-4 my-4"})})]}),e.jsxs("p",{children:["In collaboration with"," ",e.jsx("a",{href:"https://www.accenture.com/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Accenture"})," ","and"," ",e.jsx("a",{href:"https://www.cresta.com/",className:"font-bold underline text-blue-600 hover:text-blue-800 visited:text-purple-600",children:"Cresta"}),", we introduce the HELM"," ",e.jsx("span",{className:"font-bold",children:"Call Center"})," leaderboard. 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EWoK is a benchmark for evaluating world modeling in language models by testing their ability to use knowledge of a concept to match a target text with a plausible/implausible context. EWoK targets specific concepts from multiple knowledge domains known to be vital for world modeling in humans, including social interactions and spatial relations."]}),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/2405.09605",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(f,{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 Pn(){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 Medical"}),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-2",children:"With the increasing scale and impact of language models, there has also been interest interest in using language models in the medical domain. However, the capabilities and risks of these models are not well-understood, and there is significant potential for harm in the medical setting."}),e.jsxs("p",{className:"my-2",children:["To address this, we present the"," ",e.jsx("a",{className:"font-bold",href:"https://arxiv.org/abs/2405.09605",children:"HELM Medical"})," ","leaderboard for evaluation of language models in the medical domain. The HELM Medical leaderboard presents evaluations of leading general-purpose language models as well as language models fine-tuned on the medical domain. These models are evaluated on a range of medical tasks based on the benchmarks used in"," ",e.jsx("a",{className:"underline text-blue-600 hover:text-blue-800 visited:text-purple-600",href:"https://arxiv.org/abs/2212.13138",children:"Singhal et al. 2022"}),". We hope that this leaderboard encourages further work in evaluating language models on tasks from the medical domain."]}),e.jsx("div",{className:"flex flex-row justify-center my-4",children:e.jsx(f,{to:"leaderboard",className:"px-10 btn rounded-md mx-4",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(f,{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 Bn(){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:"HELM Safety"}),e.jsxs("div",{className:"flex flex-col lg:flex-row items-center gap-8",children:[e.jsxs("div",{className:"flex-1 text-xl",children:[e.jsx("p",{children:"Language models demonstrate powerful capabilities and pose significant risks. Given their widespread deployment, standardized public benchmarking of such models is vital. While language models are routinely evaluated on standard capability benchmarks, comparable standardization for benchmarking safety risks lags behind. To address this gap, we introduce HELM-Safety as a collection of 5 safety benchmarks that span 6 risk categories (e.g. violence, fraud, discrimination, sexual, harassment, deception). We present evaluation results for recent leading open weights and closed models."}),e.jsx("div",{className:"flex flex-row justify-center mt-4",children:e.jsx("a",{className:"px-10 btn rounded-md mx-4",href:"#/leaderboard",children:"Full Leaderboard"})})]}),e.jsx("div",{className:"flex-1",children:e.jsx(E,{})})]})]})}function Dn(){return window.PROJECT_ID==="lite"?e.jsx(Le,{}):window.PROJECT_ID==="instruct"?e.jsx(Zt,{}):window.PROJECT_ID==="image2struct"?e.jsx(Cn,{}):window.PROJECT_ID==="heim"?e.jsx(xn,{}):window.PROJECT_ID==="mmlu"?e.jsx(tn,{}):window.PROJECT_ID==="vhelm"?e.jsx(gn,{}):window.PROJECT_ID==="air-bench"?e.jsx(an,{}):window.PROJECT_ID==="thaiexam"?e.jsx(cn,{}):window.PROJECT_ID==="finance"?e.jsx(dn,{}):window.PROJECT_ID==="call-center"?e.jsx(bn,{}):window.PROJECT_ID==="cleva"?e.jsx(vn,{}):window.PROJECT_ID==="tables"?e.jsx(An,{}):window.PROJECT_ID==="ewok"?e.jsx(Tn,{}):window.PROJECT_ID==="medical"?e.jsx(Pn,{}):window.PROJECT_ID==="safety"?e.jsx(Bn,{}):window.PROJECT_ID==="home"?e.jsx(Sn,{}):e.jsx(Le,{})}function Un(){return e.jsx(Rs,{children:e.jsx(Is,{children:e.jsxs(D,{path:"/",element:e.jsx(xt,{}),children:[e.jsx(D,{index:!0,element:e.jsx(Dn,{})}),e.jsx(D,{path:"leaderboard",element:e.jsx(Xt,{})}),e.jsx(D,{path:"models",element:e.jsx(jt,{})}),e.jsx(D,{path:"scenarios",element:e.jsx(bt,{})}),e.jsx(D,{path:"groups",element:e.jsx(At,{})}),e.jsx(D,{path:"groups/:groupName",element:e.jsx(Et,{})}),e.jsx(D,{path:"runs",element:e.jsx(yt,{})}),e.jsx(D,{path:"runs/:runName",element:e.jsx(Jt,{})})]})})})}ce.createRoot(document.getElementById("root")).render(e.jsx(Ss.StrictMode,{children:e.jsx(Un,{})}));
diff --git a/src/helm/benchmark/static_build/index.html b/src/helm/benchmark/static_build/index.html
index 50fb952b41..7b6cc99dfc 100644
--- a/src/helm/benchmark/static_build/index.html
+++ b/src/helm/benchmark/static_build/index.html
@@ -7,7 +7,7 @@
     <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-b6ad1ddb.js"></script>
+    <script type="module" crossorigin src="./assets/index-f2118805.js"></script>
     <link rel="modulepreload" crossorigin href="./assets/react-d4a0b69b.js">
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