DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation

• URL: http://arxiv.org/abs/2405.15329v3
• Date: Sun, 08 Dec 2024 21:41:57 GMT
• Title: DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
• Authors: Minzhi Li, Zhengyuan Liu, Shumin Deng, Shafiq Joty, Nancy F. Chen, Min-Yen Kan,
• Abstract summary: Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
• Score: 75.81096662788254
• License:
• Abstract: The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated texts. They serve as scalable and economical evaluators, but the question of how reliable these evaluators are has emerged as a crucial research question. Prior research efforts in the meta-evaluation of LLMs as judges limit the prompting of an LLM to a single use to obtain a final evaluation decision. They then compute the agreement between LLMs' outputs and human labels. This lacks interpretability in understanding the evaluation capability of LLMs. In light of this challenge, we propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices. Our experiments illustrate that it not only provides a more interpretable window for how well LLMs evaluate, but also leads to improvements up to 39.6% for different LLMs on a variety of meta-evaluation benchmarks.

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