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Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

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Manage episode 434630886 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This week’s paper presents a comprehensive study of the performance of various LLMs acting as judges. The researchers leverage TriviaQA as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate them alongside human annotations which they find to have a high inter-annotator agreement. The study includes nine judge models and nine exam-taker models – both base and instruction-tuned. They assess the judge models’ alignment across different model sizes, families, and judge prompts to answer questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold.

Read it on the blog: https://arize.com/blog/judging-the-judges-llm-as-a-judge/

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

47 episodes

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iconShare
 
Manage episode 434630886 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

This week’s paper presents a comprehensive study of the performance of various LLMs acting as judges. The researchers leverage TriviaQA as a benchmark for assessing objective knowledge reasoning of LLMs and evaluate them alongside human annotations which they find to have a high inter-annotator agreement. The study includes nine judge models and nine exam-taker models – both base and instruction-tuned. They assess the judge models’ alignment across different model sizes, families, and judge prompts to answer questions about the strengths and weaknesses of this paradigm, and what potential biases it may hold.

Read it on the blog: https://arize.com/blog/judging-the-judges-llm-as-a-judge/

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

47 episodes

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