Learning to Verify Summary Facts with Fine-Grained LLM Feedback

• URL: http://arxiv.org/abs/2412.10689v1
• Date: Sat, 14 Dec 2024 05:28:44 GMT
• Title: Learning to Verify Summary Facts with Fine-Grained LLM Feedback
• Authors: Jihwan Oh, Jeonghwan Choi, Nicole Hee-Yeon Kim, Taewon Yun, Hwanjun Song,
• Abstract summary: Training automatic summary fact verifiers often faces the challenge of a lack of human-labeled data. We introduce FineSumFact, a large-scale dataset containing fine-grained factual feedback on summaries.
• Score: 15.007479147796403
• License:
• Abstract: Training automatic summary fact verifiers often faces the challenge of a lack of human-labeled data. In this paper, we explore alternative way of leveraging Large Language Model (LLM) generated feedback to address the inherent limitation of using human-labeled data. We introduce FineSumFact, a large-scale dataset containing fine-grained factual feedback on summaries. We employ 10 distinct LLMs for diverse summary generation and Llama-3-70B-Instruct for feedback. We utilize this dataset to fine-tune the lightweight open-source model Llama-3-8B-Instruct, optimizing resource efficiency while maintaining high performance. Our experimental results reveal that the model trained on extensive LLM-generated datasets surpasses that trained on smaller human-annotated datasets when evaluated using human-generated test sets. Fine-tuning fact verification models with LLM feedback can be more effective and cost-efficient than using human feedback. The dataset is available at https://github.com/DISL-Lab/FineSumFact.

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