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\ No newline at end of file diff --git a/about/index.html b/about/index.html index bf5af006..c780f777 100644 --- a/about/index.html +++ b/about/index.html @@ -13,12 +13,12 @@ - - + + -
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About Us

Zeno is designed, built, and maintained by a team of interdisciplinary researchers at Carnegie Mellon University. We span research groups in Machine Learning, Human-Computer Interaction, and Language Technologies. We are passionate about empowering people to understand the complexities of AI systems in order to design the future of responsible AI.

Team

Alex Cabrera

PhD Candidate

Alex Bäuerle

Research Scientist

Erica Fu

Undergraduate Student

Ankitha Vasudev

Masters Student

Kathy Yu

Masters Student

Josh Zhou

Undergraduate Student

Donald Bertucci

Undergraduate Student

Ameet Talwalkar

Associate Professor

Graham Neubig

Associate Professor

Jason Hong

Professor

Adam Perer

Assistant Professor

Past Members

Tianqi Wu

Masters Student

Yi-Cheng Huang

Research Associate

Sponsors & Organizations

We are thankful to our sponsors and affiliated organizations. Zeno is part of the Mozilla Technology Fund and developed partly with the support of multiple NSF grants and fellowships.

Mozilla logoMozilla logoCMU logo

Contact

If you have any questions or issues with Zeno please send an email to hello@zenoml.com, ask in our Discord, or open an issue on GitHub

Cite

Please reference our CHI 2023 paper if you would like to cite Zeno:


@inproceedings{cabrera23zeno,
  author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
  title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
  year = {2023},
  isbn = {978-1-4503-9421-5/23/04},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3544548.3581268},
  doi = {10.1145/3544548.3581268},
  booktitle = {CHI Conference on Human Factors in Computing Systems},
  location = {Hamburg, Germany},
  series = {CHI '23}
}
+
Skip to main content

About Us

Zeno is designed, built, and maintained by a team of interdisciplinary researchers at Carnegie Mellon University. We span research groups in Machine Learning, Human-Computer Interaction, and Language Technologies. We are passionate about empowering people to understand the complexities of AI systems in order to design the future of responsible AI.

Team

Alex Cabrera

PhD Candidate

Alex Bäuerle

Research Scientist

Erica Fu

Undergraduate Student

Ankitha Vasudev

Masters Student

Kathy Yu

Masters Student

Josh Zhou

Undergraduate Student

Donald Bertucci

Undergraduate Student

Ameet Talwalkar

Associate Professor

Graham Neubig

Associate Professor

Jason Hong

Professor

Adam Perer

Assistant Professor

Past Members

Tianqi Wu

Masters Student

Yi-Cheng Huang

Research Associate

Sponsors & Organizations

We are thankful to our sponsors and affiliated organizations. Zeno is part of the Mozilla Technology Fund and developed partly with the support of multiple NSF grants and fellowships.

Mozilla logoMozilla logoCMU logo

Contact

If you have any questions or issues with Zeno please send an email to hello@zenoml.com, ask in our Discord, or open an issue on GitHub

Cite

Please reference our CHI 2023 paper if you would like to cite Zeno:


@inproceedings{cabrera23zeno,
  author = {Cabrera, Ángel Alexander and Fu, Erica and Bertucci, Donald and Holstein, Kenneth and Talwalkar, Ameet and Hong, Jason I. and Perer, Adam},
  title = {Zeno: An Interactive Framework for Behavioral Evaluation of Machine Learning},
  year = {2023},
  isbn = {978-1-4503-9421-5/23/04},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3544548.3581268},
  doi = {10.1145/3544548.3581268},
  booktitle = {CHI Conference on Human Factors in Computing Systems},
  location = {Hamburg, Germany},
  series = {CHI '23}
}
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strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[217],{314:e=>{e.exports=JSON.parse('{"permalink":"/blog/tags/zenos-notes","page":1,"postsPerPage":10,"totalPages":1,"totalCount":1,"blogDescription":"Blog","blogTitle":"Blog"}')}}]); \ No newline at end of file diff --git a/assets/js/20417f73.98e56347.js b/assets/js/20417f73.98e56347.js deleted file mode 100644 index 4250b50a..00000000 --- a/assets/js/20417f73.98e56347.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[743],{7456:s=>{s.exports=JSON.parse('{"label":"introduction","permalink":"/blog/tags/introduction","allTagsPath":"/blog/tags","count":1,"unlisted":false}')}}]); \ No newline at end of file diff --git a/assets/js/4bc6dbc6.b02f1943.js b/assets/js/4bc6dbc6.b02f1943.js new file mode 100644 index 00000000..810efffb --- /dev/null +++ b/assets/js/4bc6dbc6.b02f1943.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[577],{9065:(e,n,t)=>{t.r(n),t.d(n,{assets:()=>l,contentTitle:()=>r,default:()=>c,frontMatter:()=>s,metadata:()=>i,toc:()=>h});var o=t(5893),a=t(1151);const s={slug:"newsletter-24-01",title:"Zeno's Notes on AI Evaluation | January 2024",authors:["ac","ab"],tags:["zeno's notes"]},r=void 0,i={permalink:"/blog/newsletter-24-01",source:"@site/blog/2024-01-09-newsletter.md",title:"Zeno's Notes on AI Evaluation | January 2024",description:"Welcome to the first edition of the Zeno's Notes newsletter!",date:"2024-01-09T00:00:00.000Z",formattedDate:"January 9, 2024",tags:[{label:"zeno's notes",permalink:"/blog/tags/zenos-notes"}],readingTime:3.575,hasTruncateMarker:!1,authors:[{name:"Alex Cabrera",title:"PhD Candidate @ CMU",url:"https://cabreraalex.com",imageURL:"https://cabreraalex.com/images/profile.png",key:"ac"},{name:"Alex B\xe4uerle",title:"Researcher @ CMU",url:"https://a13x.io",imageURL:"https://a13x.io/images/alex.jpeg",key:"ab"}],frontMatter:{slug:"newsletter-24-01",title:"Zeno's Notes on AI Evaluation | January 2024",authors:["ac","ab"],tags:["zeno's notes"]},unlisted:!1},l={authorsImageUrls:[void 0,void 0]},h=[{value:"\ud83c\udf0e Community",id:"-community",level:2},{value:"An In-Depth Look at Gemini's Language Abilities",id:"an-in-depth-look-at-geminis-language-abilities",level:3},{value:"HuggingFace is Dropping DROP",id:"huggingface-is-dropping-drop",level:3},{value:"\ud83d\udcf0 Evaluation News",id:"-evaluation-news",level:2},{value:"CRUXEval",id:"cruxeval",level:3},{value:"CommonGen Leaderboard",id:"commongen-leaderboard",level:3},{value:"\u2728 New in Zeno",id:"-new-in-zeno",level:2},{value:"Integrations",id:"integrations",level:3},{value:"Documentation",id:"documentation",level:3}];function d(e){const n={a:"a",em:"em",h2:"h2",h3:"h3",hr:"hr",li:"li",p:"p",strong:"strong",ul:"ul",...(0,a.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsxs)(n.p,{children:["Welcome to the first edition of the ",(0,o.jsx)(n.strong,{children:"Zeno's Notes"})," newsletter!\nEach month, we'll discuss the community's work around Zeno, interesting research and projects on AI evaluation, and new Zeno features."]}),"\n",(0,o.jsxs)(n.p,{children:["Before we dive in, we wanted to look back at the last few months since we launched ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com",children:"Zeno Hub"}),".\nOur users have created over ",(0,o.jsx)(n.strong,{children:"800 projects"})," and ",(0,o.jsx)(n.strong,{children:"1,400 slices"})," to evaluate more than ",(0,o.jsx)(n.strong,{children:"10,000 AI systems"}),"!\nThese insights have been used to author over ",(0,o.jsx)(n.strong,{children:"160 reports"}),", communicating interesting findings and insights.\nIt's exciting to see how Zeno is being used to make AI evaluation more accessible and transparent."]}),"\n",(0,o.jsx)(n.h2,{id:"-community",children:"\ud83c\udf0e Community"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Highlighting work from the Zeno community."})}),"\n",(0,o.jsx)(n.h3,{id:"an-in-depth-look-at-geminis-language-abilities",children:(0,o.jsx)(n.a,{href:"https://arxiv.org/abs/2312.11444",children:"An In-Depth Look at Gemini's Language Abilities"})}),"\n",(0,o.jsxs)(n.p,{children:["Researchers at CMU, including the Zeno team, conducted a ",(0,o.jsx)(n.a,{href:"https://x.com/gneubig/status/1737108966931673191?s=20",children:"deep dive into Gemini's language abilities"}),".\nThey compared Gemini Pro, Google's newly released LLM, with GPT-3.5-Turbo, GPT-4-Turbo, and Mixtral.\nOverall, they found that Gemini approaches but lags behind GPT-3.5-Turbo in all English tasks, yet performs better in translation into languages it supports.\nFor more detailed results, ",(0,o.jsx)(n.a,{href:"https://arxiv.org/abs/2312.11444",children:"read the paper"})," or explore the code on ",(0,o.jsx)(n.a,{href:"https://t.co/S7S9473xtP",children:"GitHub"}),".\nEach section of the paper is linked to a Zeno report for further exploration!"]}),"\n",(0,o.jsx)(n.h3,{id:"huggingface-is-dropping-drop",children:(0,o.jsx)(n.a,{href:"https://huggingface.co/blog/leaderboard-drop-dive",children:"HuggingFace is Dropping DROP"})}),"\n",(0,o.jsxs)(n.p,{children:["The HuggingFace ",(0,o.jsx)(n.a,{href:"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard",children:"Open LLM Leaderboard"})," is a popular repository for comparing new LLMs. HuggingFace recently added ",(0,o.jsx)(n.a,{href:"https://twitter.com/clefourrier/status/1722555555338956840",children:"three new benchmarks"})," to their leaderboard to better represent real-world performance.\nAfter receiving feedback from the community, they noticed significant fluctuations in the scores for one benchmark, DROP.\n",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/report/1255/DROP%20Benchmark%20Exploration",children:"Using Zeno"}),", they uncovered the reason behind the variance and decided to remove DROP from the leaderboard until a revised version of the benchmark is developed."]}),"\n",(0,o.jsx)(n.h2,{id:"-evaluation-news",children:"\ud83d\udcf0 Evaluation News"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Interesting news from the world of AI evaluation."})}),"\n",(0,o.jsx)(n.h3,{id:"cruxeval",children:(0,o.jsx)(n.a,{href:"https://crux-eval.github.io/",children:"CRUXEval"})}),"\n",(0,o.jsx)(n.p,{children:"Researchers from MIT and Meta AI Research released new evaluation dataset for code reasoning, understanding, and execution.\nInstead of having models generate code, CRUXEval asks models to either predict the output or input of a given function based on its signature.\nThis dataset, which includes 800 python functions, supplements classic code generation datasets such as HumanEval and MBPP.\nThey compared multiple open and closed-source models on their new benchmarks and found that there is quite a bit of room for improvement."}),"\n",(0,o.jsx)(n.h3,{id:"commongen-leaderboard",children:(0,o.jsx)(n.a,{href:"https://inklab.usc.edu/CommonGen/leaderboard.html",children:"CommonGen Leaderboard"})}),"\n",(0,o.jsxs)(n.p,{children:["CommonGen is a challenging benchmark task asking models to generate coherent sentences describing everyday scenarios.\nThe reseaerchers behind the benchmark, from USC, Allen AI, and UW, recently updated their ",(0,o.jsx)(n.a,{href:"https://github.com/allenai/CommonGen-Eval",children:"eval repository"})," with a ",(0,o.jsx)(n.a,{href:"https://inklab.usc.edu/CommonGen/leaderboard.html",children:"new leaderboard"})," for the task, showing how state-of-the-art models, including GPT-4, perform significantly worse than humans.\nThe authors argue that the task is so hard because it requires relational reasoning using background common sense knowledge and the models need to be able to generalize to unseen concept combinations."]}),"\n",(0,o.jsx)(n.h2,{id:"-new-in-zeno",children:"\u2728 New in Zeno"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Updates to Zeno that you'll love."})}),"\n",(0,o.jsx)(n.h3,{id:"integrations",children:"Integrations"}),"\n",(0,o.jsxs)(n.p,{children:["We've been focusing on making it even easier for you to analyze your evaluation results in Zeno by ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/integrations/",children:"integrating Zeno into other AI evaluation frameworks"}),".\nYou can now directly upload your model outputs if you're using the ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness",children:"EleutherAI LM Evaluation Harness"})," or the ",(0,o.jsx)(n.a,{href:"https://docs.ragas.io/en/latest/index.html",children:"Ragas Framework"}),"."]}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:[(0,o.jsx)(n.strong,{children:"Ragas"})," is a library for model-graded evaluation of RAG applications. We've added ",(0,o.jsx)(n.a,{href:"https://docs.ragas.io/en/latest/howtos/integrations/zeno.html",children:"a detailed tutorial"})," on how to use Zeno to investigate your evaluation results. You can view an example of this in Zeno ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/project/b35c83b8-0b22-4b9c-aedb-80964011d7a7/ragas%20FICA%20eval",children:"here"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:[(0,o.jsx)(n.strong,{children:"EleutherAI LM Evaluation Harness"})," is a popular library for running LLM benchmarks. We wrote a script that allows you to directly upload all your evaluation data as a Zeno project, enabling you to compare different models across various benchmarks provided by EleutherAI. To start visualizing your LM Evaluation Harness data in Zeno, follow ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness#visualizing-results",children:"these instructions"})," or take a look at our ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-zeno.ipynb",children:"example notebook"}),". We've already used this integration for some of our own projects, such as ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/project/ba44d31c-9e02-4330-bdbe-0760dfe85dc4/Mamba%20Eval_hellaswag",children:"this evaluation of the Mamba architecture"}),"!"]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h3,{id:"documentation",children:"Documentation"}),"\n",(0,o.jsxs)(n.p,{children:["We've also been working on improving our documentation to make it easier for you to get started with Zeno.\nThis includes ",(0,o.jsx)(n.a,{href:"http://localhost:3000/docs/examples/",children:"use cases"}),", ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/tutorials/",children:"tutorials"}),", and ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/integrations/",children:"integration guides"}),".\nIf you have any suggestions for what you'd like to see in our documentation, please let us know!"]}),"\n",(0,o.jsx)(n.hr,{}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsxs)(n.em,{children:["If you have questions about Zeno or anything we've highlighted in this newsletter, have ideas for new Zeno features or content for a future issue of Zeno's Notes, or simply want to say hi, get in touch via ",(0,o.jsx)(n.a,{href:"mailto:hello@zenoml.com",children:"email"})," or join our ",(0,o.jsx)(n.a,{href:"https://discord.gg/km62pDKAkE",children:"Discord"}),"."]})})]})}function c(e={}){const{wrapper:n}={...(0,a.a)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>i,a:()=>r});var o=t(7294);const a={},s=o.createContext(a);function r(e){const n=o.useContext(s);return o.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function i(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:r(e.components),o.createElement(s.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/5ad2500b.2310f764.js b/assets/js/5ad2500b.2310f764.js deleted file mode 100644 index 664dd730..00000000 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News",id:"-evaluation-news",level:2},{value:"CRUXEval",id:"cruxeval",level:3},{value:"CommonGen Leaderboard",id:"commongen-leaderboard",level:3},{value:"\u2728 New in Zeno",id:"-new-in-zeno",level:2},{value:"Integrations",id:"integrations",level:3},{value:"Documentation",id:"documentation",level:3}];function d(e){const n={a:"a",em:"em",h2:"h2",h3:"h3",hr:"hr",li:"li",p:"p",strong:"strong",ul:"ul",...(0,a.a)(),...e.components};return(0,o.jsxs)(o.Fragment,{children:[(0,o.jsxs)(n.p,{children:["Welcome to the first edition of the ",(0,o.jsx)(n.strong,{children:"Zeno's Notes"})," newsletter!\nEach month, we'll discuss the community's work around Zeno, interesting research and projects on AI evaluation, and new Zeno features."]}),"\n",(0,o.jsxs)(n.p,{children:["Before we dive in, we wanted to look back at the last few months since we launched ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com",children:"Zeno Hub"}),".\nOur users have created over ",(0,o.jsx)(n.strong,{children:"800 projects"})," and ",(0,o.jsx)(n.strong,{children:"1,400 slices"})," to evaluate more than ",(0,o.jsx)(n.strong,{children:"10,000 AI systems"}),"!\nThese insights have been used to author over ",(0,o.jsx)(n.strong,{children:"160 reports"}),", communicating interesting findings and insights.\nIt's exciting to see how Zeno is being used to make AI evaluation more accessible and transparent."]}),"\n",(0,o.jsx)(n.h2,{id:"-community",children:"\ud83c\udf0e Community"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Highlighting work from the Zeno community."})}),"\n",(0,o.jsx)(n.h3,{id:"an-in-depth-look-at-geminis-language-abilities",children:(0,o.jsx)(n.a,{href:"https://arxiv.org/abs/2312.11444",children:"An In-Depth Look at Gemini's Language Abilities"})}),"\n",(0,o.jsxs)(n.p,{children:["Researchers at CMU, including the Zeno team, conducted a ",(0,o.jsx)(n.a,{href:"https://x.com/gneubig/status/1737108966931673191?s=20",children:"deep dive into Gemini's language abilities"}),".\nThey compared Gemini Pro, Google's newly released LLM, with GPT-3.5-Turbo, GPT-4-Turbo, and Mixtral.\nOverall, they found that Gemini approaches but lags behind GPT-3.5-Turbo in all English tasks, yet performs better in translation into languages it supports.\nFor more detailed results, ",(0,o.jsx)(n.a,{href:"https://arxiv.org/abs/2312.11444",children:"read the paper"})," or explore the code on ",(0,o.jsx)(n.a,{href:"https://t.co/S7S9473xtP",children:"GitHub"}),".\nEach section of the paper is linked to a Zeno report for further exploration!"]}),"\n",(0,o.jsx)(n.h3,{id:"huggingface-is-dropping-drop",children:(0,o.jsx)(n.a,{href:"https://huggingface.co/blog/leaderboard-drop-dive",children:"HuggingFace is Dropping DROP"})}),"\n",(0,o.jsxs)(n.p,{children:["The HuggingFace ",(0,o.jsx)(n.a,{href:"https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard",children:"Open LLM Leaderboard"})," is a popular repository for comparing new LLMs. HuggingFace recently added ",(0,o.jsx)(n.a,{href:"https://twitter.com/clefourrier/status/1722555555338956840",children:"three new benchmarks"})," to their leaderboard to better represent real-world performance.\nAfter receiving feedback from the community, they noticed significant fluctuations in the scores for one benchmark, DROP.\n",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/report/1255/DROP%20Benchmark%20Exploration",children:"Using Zeno"}),", they uncovered the reason behind the variance and decided to remove DROP from the leaderboard until a revised version of the benchmark is developed."]}),"\n",(0,o.jsx)(n.h2,{id:"-evaluation-news",children:"\ud83d\udcf0 Evaluation News"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Interesting news from the world of AI evaluation."})}),"\n",(0,o.jsx)(n.h3,{id:"cruxeval",children:(0,o.jsx)(n.a,{href:"https://crux-eval.github.io/",children:"CRUXEval"})}),"\n",(0,o.jsx)(n.p,{children:"Researchers from MIT and Meta AI Research released new evaluation dataset for code reasoning, understanding, and execution.\nInstead of having models generate code, CRUXEval asks models to either predict the output or input of a given function based on its signature.\nThis dataset, which includes 800 python functions, supplements classic code generation datasets such as HumanEval and MBPP.\nThey compared multiple open and closed-source models on their new benchmarks and found that there is quite a bit of room for improvement."}),"\n",(0,o.jsx)(n.h3,{id:"commongen-leaderboard",children:(0,o.jsx)(n.a,{href:"https://inklab.usc.edu/CommonGen/leaderboard.html",children:"CommonGen Leaderboard"})}),"\n",(0,o.jsxs)(n.p,{children:["CommonGen is a challenging benchmark task asking models to generate coherent sentences describing everyday scenarios.\nThe reseaerchers behind the benchmark, from USC, Allen AI, and UW, recently updated their ",(0,o.jsx)(n.a,{href:"https://github.com/allenai/CommonGen-Eval",children:"eval repository"})," with a ",(0,o.jsx)(n.a,{href:"https://inklab.usc.edu/CommonGen/leaderboard.html",children:"new leaderboard"})," for the task, showing how state-of-the-art models, including GPT-4, perform significantly worse than humans.\nThe authors argue that the task is so hard because it requires relational reasoning using background common sense knowledge and the models need to be able to generalize to unseen concept combinations."]}),"\n",(0,o.jsx)(n.h2,{id:"-new-in-zeno",children:"\u2728 New in Zeno"}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsx)(n.em,{children:"Updates to Zeno that you'll love."})}),"\n",(0,o.jsx)(n.h3,{id:"integrations",children:"Integrations"}),"\n",(0,o.jsxs)(n.p,{children:["We've been focusing on making it even easier for you to analyze your evaluation results in Zeno by ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/integrations/",children:"integrating Zeno into other AI evaluation frameworks"}),".\nYou can now directly upload your model outputs if you're using the ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness",children:"EleutherAI LM Evaluation Harness"})," or the ",(0,o.jsx)(n.a,{href:"https://docs.ragas.io/en/latest/index.html",children:"Ragas Framework"}),"."]}),"\n",(0,o.jsxs)(n.ul,{children:["\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:[(0,o.jsx)(n.strong,{children:"Ragas"})," is a library for model-graded evaluation of RAG applications. We've added ",(0,o.jsx)(n.a,{href:"https://docs.ragas.io/en/latest/howtos/integrations/zeno.html",children:"a detailed tutorial"})," on how to use Zeno to investigate your evaluation results. You can view an example of this in Zeno ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/project/b35c83b8-0b22-4b9c-aedb-80964011d7a7/ragas%20FICA%20eval",children:"here"}),"."]}),"\n"]}),"\n",(0,o.jsxs)(n.li,{children:["\n",(0,o.jsxs)(n.p,{children:[(0,o.jsx)(n.strong,{children:"EleutherAI LM Evaluation Harness"})," is a popular library for running LLM benchmarks. We wrote a script that allows you to directly upload all your evaluation data as a Zeno project, enabling you to compare different models across various benchmarks provided by EleutherAI. To start visualizing your LM Evaluation Harness data in Zeno, follow ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness#visualizing-results",children:"these instructions"})," or take a look at our ",(0,o.jsx)(n.a,{href:"https://github.com/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-zeno.ipynb",children:"example notebook"}),". We've already used this integration for some of our own projects, such as ",(0,o.jsx)(n.a,{href:"https://hub.zenoml.com/project/ba44d31c-9e02-4330-bdbe-0760dfe85dc4/Mamba%20Eval_hellaswag",children:"this evaluation of the Mamba architecture"}),"!"]}),"\n"]}),"\n"]}),"\n",(0,o.jsx)(n.h3,{id:"documentation",children:"Documentation"}),"\n",(0,o.jsxs)(n.p,{children:["We've also been working on improving our documentation to make it easier for you to get started with Zeno.\nThis includes ",(0,o.jsx)(n.a,{href:"http://localhost:3000/docs/examples/",children:"use cases"}),", ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/tutorials/",children:"tutorials"}),", and ",(0,o.jsx)(n.a,{href:"https://zenoml.com/docs/integrations/",children:"integration guides"}),".\nIf you have any suggestions for what you'd like to see in our documentation, please let us know!"]}),"\n",(0,o.jsx)(n.hr,{}),"\n",(0,o.jsx)(n.p,{children:(0,o.jsxs)(n.em,{children:["If you have questions about Zeno or anything we've highlighted in this newsletter, have ideas for new Zeno features or content for a future issue of Zeno's Notes, or simply want to say hi, get in touch via ",(0,o.jsx)(n.a,{href:"mailto:hello@zenoml.com",children:"email"})," or join our ",(0,o.jsx)(n.a,{href:"https://discord.gg/km62pDKAkE",children:"Discord"}),"."]})})]})}function c(e={}){const{wrapper:n}={...(0,a.a)(),...e.components};return n?(0,o.jsx)(n,{...e,children:(0,o.jsx)(d,{...e})}):d(e)}},1151:(e,n,t)=>{t.d(n,{Z:()=>i,a:()=>r});var o=t(7294);const a={},s=o.createContext(a);function r(e){const n=o.useContext(s);return o.useMemo((function(){return"function"==typeof e?e(n):{...n,...e}}),[n,e])}function i(e){let n;return n=e.disableParentContext?"function"==typeof e.components?e.components(a):e.components||a:r(e.components),o.createElement(s.Provider,{value:n},e.children)}}}]); \ No newline at end of file diff --git a/assets/js/b2f554cd.3f8870ff.js b/assets/js/b2f554cd.3f8870ff.js new file mode 100644 index 00000000..f6ac6012 --- /dev/null +++ b/assets/js/b2f554cd.3f8870ff.js @@ -0,0 +1 @@ +"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[477],{10:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"newsletter-24-01","metadata":{"permalink":"/blog/newsletter-24-01","source":"@site/blog/2024-01-09-newsletter.md","title":"Zeno\'s Notes on AI Evaluation | January 2024","description":"Welcome to the first edition of the Zeno\'s Notes newsletter!","date":"2024-01-09T00:00:00.000Z","formattedDate":"January 9, 2024","tags":[{"label":"zeno\'s notes","permalink":"/blog/tags/zenos-notes"}],"readingTime":3.575,"hasTruncateMarker":false,"authors":[{"name":"Alex Cabrera","title":"PhD Candidate @ CMU","url":"https://cabreraalex.com","imageURL":"https://cabreraalex.com/images/profile.png","key":"ac"},{"name":"Alex B\xe4uerle","title":"Researcher @ CMU","url":"https://a13x.io","imageURL":"https://a13x.io/images/alex.jpeg","key":"ab"}],"frontMatter":{"slug":"newsletter-24-01","title":"Zeno\'s Notes on AI Evaluation | January 2024","authors":["ac","ab"],"tags":["zeno\'s notes"]},"unlisted":false},"content":"Welcome to the first edition of the **Zeno\'s Notes** newsletter!\\nEach month, we\'ll discuss the community\'s work around Zeno, interesting research and projects on AI evaluation, and new Zeno features.\\n\\nBefore we dive in, we wanted to look back at the last few months since we launched [Zeno Hub](https://hub.zenoml.com).\\nOur users have created over **800 projects** and **1,400 slices** to evaluate more than **10,000 AI systems**!\\nThese insights have been used to author over **160 reports**, communicating interesting findings and insights.\\nIt\'s exciting to see how Zeno is being used to make AI evaluation more accessible and transparent.\\n\\n## \ud83c\udf0e Community\\n\\n_Highlighting work from the Zeno community._\\n\\n### [An In-Depth Look at Gemini\'s Language Abilities](https://arxiv.org/abs/2312.11444)\\n\\nResearchers at CMU, including the Zeno team, conducted a [deep dive into Gemini\'s language abilities](https://x.com/gneubig/status/1737108966931673191?s=20).\\nThey compared Gemini Pro, Google\'s newly released LLM, with GPT-3.5-Turbo, GPT-4-Turbo, and Mixtral.\\nOverall, they found that Gemini approaches but lags behind GPT-3.5-Turbo in all English tasks, yet performs better in translation into languages it supports.\\nFor more detailed results, [read the paper](https://arxiv.org/abs/2312.11444) or explore the code on [GitHub](https://t.co/S7S9473xtP).\\nEach section of the paper is linked to a Zeno report for further exploration!\\n\\n### [HuggingFace is Dropping DROP](https://huggingface.co/blog/leaderboard-drop-dive)\\n\\nThe HuggingFace [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) is a popular repository for comparing new LLMs. HuggingFace recently added [three new benchmarks](https://twitter.com/clefourrier/status/1722555555338956840) to their leaderboard to better represent real-world performance.\\nAfter receiving feedback from the community, they noticed significant fluctuations in the scores for one benchmark, DROP.\\n[Using Zeno](https://hub.zenoml.com/report/1255/DROP%20Benchmark%20Exploration), they uncovered the reason behind the variance and decided to remove DROP from the leaderboard until a revised version of the benchmark is developed.\\n\\n## \ud83d\udcf0 Evaluation News\\n\\n_Interesting news from the world of AI evaluation._\\n\\n### [CRUXEval](https://crux-eval.github.io/)\\n\\nResearchers from MIT and Meta AI Research released new evaluation dataset for code reasoning, understanding, and execution.\\nInstead of having models generate code, CRUXEval asks models to either predict the output or input of a given function based on its signature.\\nThis dataset, which includes 800 python functions, supplements classic code generation datasets such as HumanEval and MBPP.\\nThey compared multiple open and closed-source models on their new benchmarks and found that there is quite a bit of room for improvement.\\n\\n### [CommonGen Leaderboard](https://inklab.usc.edu/CommonGen/leaderboard.html)\\n\\nCommonGen is a challenging benchmark task asking models to generate coherent sentences describing everyday scenarios.\\nThe reseaerchers behind the benchmark, from USC, Allen AI, and UW, recently updated their [eval repository](https://github.com/allenai/CommonGen-Eval) with a [new leaderboard](https://inklab.usc.edu/CommonGen/leaderboard.html) for the task, showing how state-of-the-art models, including GPT-4, perform significantly worse than humans.\\nThe authors argue that the task is so hard because it requires relational reasoning using background common sense knowledge and the models need to be able to generalize to unseen concept combinations.\\n\\n## \u2728 New in Zeno\\n\\n_Updates to Zeno that you\'ll love._\\n\\n### Integrations\\n\\nWe\'ve been focusing on making it even easier for you to analyze your evaluation results in Zeno by [integrating Zeno into other AI evaluation frameworks](https://zenoml.com/docs/integrations/).\\nYou can now directly upload your model outputs if you\'re using the [EleutherAI LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) or the [Ragas Framework](https://docs.ragas.io/en/latest/index.html).\\n\\n- **Ragas** is a library for model-graded evaluation of RAG applications. We\'ve added [a detailed tutorial](https://docs.ragas.io/en/latest/howtos/integrations/zeno.html) on how to use Zeno to investigate your evaluation results. You can view an example of this in Zeno [here](https://hub.zenoml.com/project/b35c83b8-0b22-4b9c-aedb-80964011d7a7/ragas%20FICA%20eval).\\n\\n- **EleutherAI LM Evaluation Harness** is a popular library for running LLM benchmarks. We wrote a script that allows you to directly upload all your evaluation data as a Zeno project, enabling you to compare different models across various benchmarks provided by EleutherAI. To start visualizing your LM Evaluation Harness data in Zeno, follow [these instructions](https://github.com/EleutherAI/lm-evaluation-harness#visualizing-results) or take a look at our [example notebook](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/examples/visualize-zeno.ipynb). We\'ve already used this integration for some of our own projects, such as [this evaluation of the Mamba architecture](https://hub.zenoml.com/project/ba44d31c-9e02-4330-bdbe-0760dfe85dc4/Mamba%20Eval_hellaswag)!\\n\\n### Documentation\\n\\nWe\'ve also been working on improving our documentation to make it easier for you to get started with Zeno.\\nThis includes [use cases](http://localhost:3000/docs/examples/), [tutorials](https://zenoml.com/docs/tutorials/), and [integration guides](https://zenoml.com/docs/integrations/).\\nIf you have any suggestions for what you\'d like to see in our documentation, please let us know!\\n\\n---\\n\\n_If you have questions about Zeno or anything we\'ve highlighted in this newsletter, have ideas for new Zeno features or content for a future issue of Zeno\'s Notes, or simply want to say hi, get in touch via [email](mailto:hello@zenoml.com) or join our [Discord](https://discord.gg/km62pDKAkE)._"}]}')}}]); \ No newline at end of file diff --git a/assets/js/b2f554cd.c52f296b.js b/assets/js/b2f554cd.c52f296b.js deleted file mode 100644 index a1aa3e54..00000000 --- a/assets/js/b2f554cd.c52f296b.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkzeno_docs=self.webpackChunkzeno_docs||[]).push([[477],{10:e=>{e.exports=JSON.parse('{"blogPosts":[{"id":"welcome-post","metadata":{"permalink":"/blog/welcome-post","source":"@site/blog/2022-08-26-welcome.md","title":"Zeno: Interactive Model Evaluation","description":"Model evaluation is a key part of the machine learning development process.","date":"2022-08-26T00:00:00.000Z","formattedDate":"August 26, 2022","tags":[{"label":"introduction","permalink":"/blog/tags/introduction"}],"readingTime":0.67,"hasTruncateMarker":false,"authors":[{"name":"Alex Cabrera","title":"PhD Student @ CMU","url":"https://cabreraalex.com","image_url":"https://cabreraalex.com/images/profile.png","imageURL":"https://cabreraalex.com/images/profile.png"}],"frontMatter":{"slug":"welcome-post","title":"Zeno: Interactive Model Evaluation","authors":{"name":"Alex Cabrera","title":"PhD Student @ CMU","url":"https://cabreraalex.com","image_url":"https://cabreraalex.com/images/profile.png","imageURL":"https://cabreraalex.com/images/profile.png"},"tags":["introduction"]},"unlisted":false},"content":"Model evaluation is a key part of the machine learning development process.\\nEvaluation often stops at measuring metrics such as accuracy, but aggregate metrics are a limited view of model performance.\\nIt\'s important to look at specific failures or subgroups of data to develop a nuanced view of model performance and discover issues such as [biases](http://gendershades.org/) and [safety concerns](https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg).\\n\\n![safety concerns](/img/overall.png)\\n\\n[**Zeno**](https://zenoml.com) is a framework for fine-grained evaluation of diverse machine learning models.\\nZeno is primarily a UI for interactively exploring your data, model outputs, and metrics.\\nYou can interactively filter your data using metadata and create slices to track and compare model performance.\\nLastly, you can create visualizations and reports to summarize model performance and share insights with other stakeholders.\\n\\n### Get Started\\n\\nInterested in using Zeno? 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