Enlarge / A Rock'em Sock'em AI model battle. (credit: CSA Images)
As the AI landscape has expanded to include dozens of distinct large language models (LLMs), debates over which model provides the "best" answers for any given prompt have also proliferated (Ars has even delved into these kinds of debates a few times in recent months). For those looking for a more rigorous way of comparing various models, the folks over at the Large Model Systems Organization (LMSys) have set up Chatbot Arena, a platform for generating Elo-style rankings for LLMs based on a crowdsourced blind-testing website.
Chatbot Arena users can enter any prompt they can think of into the site's form to see side-by-side responses from two randomly selected models. The identity of each model is initially hidden, and results are voided if the model reveals its identity in the response itself.
The user then gets to pick which model provided what they judge to be the "better" result, with additional options for a "tie" or "both are bad." Only after providing a pairwise ranking does the user get to see which models they were judging, though a separate "side-by-side" section of the site lets users pick two specific models to compare (without the ability to contribute a vote on the result).
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Enlarge / A Rock'em Sock'em AI model battle. (credit: CSA Images)
As the AI landscape has expanded to include dozens of distinct large language models (LLMs), debates over which model provides the "best" answers for any given prompt have also proliferated (Ars has even delved into these kinds of debates a few times in recent months). For those looking for a more rigorous way of comparing various models, the folks over at the Large Model Systems Organization (LMSys) have set up Chatbot Arena, a platform for generating Elo-style rankings for LLMs based on a crowdsourced blind-testing website.
Chatbot Arena users can enter any prompt they can think of into the site's form to see side-by-side responses from two randomly selected models. The identity of each model is initially hidden, and results are voided if the model reveals its identity in the response itself.
The user then gets to pick which model provided what they judge to be the "better" result, with additional options for a "tie" or "both are bad." Only after providing a pairwise ranking does the user get to see which models they were judging, though a separate "side-by-side" section of the site lets users pick two specific models to compare (without the ability to contribute a vote on the result).
Read 10 remaining paragraphs | Comments
December 13, 2023 at 11:41PM
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