The Generative AI Paradox on Evaluation: What It Can Solve, It May Not Evaluate

This paper explores the assumption that Large Language Models (LLMs) skilled in generation tasks are equally adept as evaluators. We assess the performance of three LLMs and one open-source LM in Question-Answering (QA) and evaluation tasks using the TriviaQA (Joshi et al., 2017) dataset. Results indicate a significant disparity, with LLMs exhibiting lower performance in evaluation tasks compared to generation tasks. Intriguingly, we discover instances of unfaithful evaluation where models accurately evaluate answers in areas where they lack competence, underscoring …

adept cs.ai cs.cl dataset evaluation generative language language models large language large language models llms paper paradox performance question skilled solve tasks