Self-Evaluation of Large Language Model based on Glass-box Features

• URL: http://arxiv.org/abs/2403.04222v2
• Date: Fri, 27 Sep 2024 07:08:10 GMT
• Title: Self-Evaluation of Large Language Model based on Glass-box Features
• Authors: Hui Huang, Yingqi Qu, Jing Liu, Muyun Yang, Bing Xu, Tiejun Zhao, Wenpeng Lu,
• Abstract summary: 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. We explore the utility of glass-box features under the scenario of self-evaluation, namely applying an LLM to evaluate its own output.
• Score: 32.425566330495776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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