The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods. Existing works primarily rely on external evaluators, focusing on training and prompting strategies. However, a crucial aspect, model-aware glass-box features, is overlooked. In this study, we explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output. We investigate various glass-box feature groups and discovered that the softmax distribution serves as a reliable quality indicator for self-evaluation. Experimental results on public benchmarks validate the feasibility of self-evaluation of LLMs using glass-box features.
@article{arxiv.2403.04222,
title = {Self-Evaluation of Large Language Model based on Glass-box Features},
author = {Hui Huang and Yingqi Qu and Jing Liu and Muyun Yang and Bing Xu and Tiejun Zhao and Wenpeng Lu},
journal= {arXiv preprint arXiv:2403.04222},
year = {2024}
}