Compare without Despair: Reliable Preference Evaluation with Generation Separability
This addresses the challenge of reliable preference evaluation for large language models, which is crucial for benchmarking and ranking, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of inconsistent preference ratings in human evaluation of generated language by introducing a meta-evaluation measure called separability, which estimates how suitable a test instance is for pairwise preference evaluation, and shows that instances with high separability yield more consistent ratings from both human- and auto-raters.
Human evaluation of generated language through pairwise preference judgments is pervasive. However, under common scenarios, such as when generations from a model pair are very similar, or when stochastic decoding results in large variations in generations, it results in inconsistent preference ratings. We address these challenges by introducing a meta-evaluation measure, separability, which estimates how suitable a test instance is for pairwise preference evaluation. For a candidate test instance, separability samples multiple generations from a pair of models, and measures how distinguishable the two sets of generations are. Our experiments show that instances with high separability values yield more consistent preference ratings from both human- and auto-raters. Further, the distribution of separability allows insights into which test benchmarks are more valuable for comparing models. Finally, we incorporate separability into ELO ratings, accounting for how suitable each test instance might be for reliably ranking LLMs. Overall, separability has implications for consistent, efficient and robust preference evaluation of LLMs with both human- and auto-raters.