Deep Papers LLMs as Judges: A Comprehensive Survey on LLM-Based Evaluation Methods
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Dec 23, 2024 Explore the fascinating world of large language models as judges. Discover their benefits over traditional methods, including enhanced accuracy and consistency. Delve into the various evaluation methodologies and the crucial role human evaluators play. Learn about techniques for improving model performance and the applications in summarization and retrieval-augmented generation. The discussion also highlights significant limitations and ethical concerns, emphasizing the need for audits and domain expertise to ensure responsible AI use.
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LLM as Judge Intro
- Use LLMs as judges to evaluate LLM application outputs for quality and relevance.
- LLMs offer scalability, consistency, and interpretability compared to human annotation.
Evaluation Input Types
- Employ pairwise evaluation to compare different LLM outputs or experimental changes.
- Use list-wise evaluation for ranking multiple outputs generated by your LLM.
ChatGPT's Pairwise Evaluation
- ChatGPT uses pairwise evaluation by occasionally asking users to compare two responses.
- This method helps improve their model by leveraging customer feedback.