Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on the instruction. However, previous studies have mainly focused on coarse-grained evaluation (i.e. overall preference-based evaluation), which limits interpretability since it does not consider the nature of user instructions that require instance-wise skill composition. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment Skill Sets), a fine-grained evaluation protocol for both human-based and model-based evaluation which decomposes coarse-level scoring to a skill set-level scoring for each instruction. We experimentally observe that the fine-graininess of evaluation is crucial for attaining a holistic view of model performance and increasing the reliability of the evaluation. Using FLASK, we compare multiple open-source and proprietary LLMs and observe a high correlation between model-based and human-based evaluations. We publicly release the evaluation data and code implementation at https://github.com/kaistAI/FLASK.

翻译：大语言模型的评估具有挑战性，因为指令遵循需要与人类价值观对齐，且所需的技能集因指令而异。然而，以往研究主要关注粗粒度评估（即基于整体偏好的评估），这限制了可解释性，因为它未考虑用户指令需要实例级技能组合的本质。本文提出FLASK（基于对齐技能集的细粒度语言模型评估），一种面向人类评估和模型评估的细粒度评估协议，将粗粒度评分分解为每个指令的技能集级评分。实验观察表明，评估的细粒度性对于全面了解模型表现和提高评估可靠性至关重要。利用FLASK，我们比较了多个开源和专有大型语言模型，并观察到模型评估与人类评估之间的高度相关性。我们已在https://github.com/kaistAI/FLASK 公开发布评估数据和代码实现。