Automatic Evaluation of Attribution by Large Language Models

• URL: http://arxiv.org/abs/2305.06311v2
• Date: Sat, 7 Oct 2023 22:46:33 GMT
• Title: Automatic Evaluation of Attribution by Large Language Models
• Authors: Xiang Yue, Boshi Wang, Ziru Chen, Kai Zhang, Yu Su and Huan Sun
• Abstract summary: We investigate the automatic evaluation of attribution given by large language models (LLMs) We begin by defining different types of attribution errors, and then explore two approaches for automatic evaluation. We manually curate a set of test examples covering 12 domains from a generative search engine, New Bing.
• Score: 24.443271739599194
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A recent focus of large language model (LLM) development, as exemplified by generative search engines, is to incorporate external references to generate and support its claims. However, evaluating the attribution, i.e., verifying whether the generated statement is fully supported by the cited reference, remains an open problem. Although human evaluation is common practice, it is costly and time-consuming. In this paper, we investigate the automatic evaluation of attribution given by LLMs. We begin by defining different types of attribution errors, and then explore two approaches for automatic evaluation: prompting LLMs and fine-tuning smaller LMs. The fine-tuning data is repurposed from related tasks such as question answering, fact-checking, natural language inference, and summarization. We manually curate a set of test examples covering 12 domains from a generative search engine, New Bing. Our results on this curated test set and simulated examples from existing benchmarks highlight both promising signals and challenges. We hope our problem formulation, testbeds, and findings will help lay the foundation for future studies on this important problem.

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