Cover Image for Community Paper Reading: Judging the Judges – Evaluating Alignment and Vulnerabilities in LLMs-as-Judges
Cover Image for Community Paper Reading: Judging the Judges – Evaluating Alignment and Vulnerabilities in LLMs-as-Judges
Avatar for Arize AI
Presented by
Arize AI
Hosted By

Community Paper Reading: Judging the Judges – Evaluating Alignment and Vulnerabilities in LLMs-as-Judges

Zoom
Registration
Past Event
Welcome! To join the event, please register below.
About Event

This week's paper, Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges, 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.

Avatar for Arize AI
Presented by
Arize AI
Hosted By