Evaluating the Consistency of LLM Evaluators

Autor:	Lee, Noah, Hong, Jiwoo, Thorne, James
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	Large language models (LLMs) have shown potential as general evaluators along with the evident benefits of speed and cost. While their correlation against human annotators has been widely studied, consistency as evaluators is still understudied, raising concerns about the reliability of LLM evaluators. In this paper, we conduct extensive studies on the two aspects of consistency in LLM evaluations, Self-Consistency (SC) and Inter-scale Consistency (IC), on different scoring scales and criterion granularity with open-source and proprietary models. Our comprehensive analysis demonstrates that strong proprietary models are not necessarily consistent evaluators, highlighting the importance of considering consistency in assessing the capability of LLM evaluators. Comment: Accepted to COLING 2025
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.00543 Zobrazit plný text záznamu View this record from Arxiv