DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation
This addresses the need for more interpretable and accurate LLM-based evaluation methods, which is incremental as it builds on prior meta-evaluation approaches.
The paper tackles the problem of unreliable evaluation by Large Language Models (LLMs) as judges by proposing a decomposition and aggregation method, resulting in improvements of up to 39.6% on meta-evaluation benchmarks.
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.