Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs

• URL: http://arxiv.org/abs/2401.14640v1
• Date: Fri, 26 Jan 2024 04:11:07 GMT
• Title: Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs
• Authors: Nan Hu, Jiaoyan Chen, Yike Wu, Guilin Qi, Sheng Bi, Tongtong Wu and Jeff Z. Pan
• Abstract summary: We introduce a set of fine-grained categories for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark. Our analysis reveals that existing evaluators perform poorly under fine-grained attribution settings and exhibit weaknesses in complex citation-statement reasoning.
• Score: 35.089203283068635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention. The current methods for automatically evaluating the attribution, which are often based on Large Language Models (LLMs), are still inadequate, particularly in recognizing subtle differences between attributions, and complex relationships between citations and statements. To compare these attribution evaluation methods and develop new ones, we introduce a set of fine-grained categories (i.e., supportive, insufficient, contradictory and irrelevant) for measuring the attribution, and develop a Complex Attributed Question Answering (CAQA) benchmark by leveraging knowledge graphs (KGs) for automatically generating attributions of different categories to question-answer pairs. Our analysis reveals that existing evaluators perform poorly under fine-grained attribution settings and exhibit weaknesses in complex citation-statement reasoning. Our CAQA benchmark, validated with human annotations, emerges as a promising tool for selecting and developing LLM attribution evaluators.

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