Learning to Verify Summary Facts with Fine-Grained LLM Feedback
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915063682564096 |
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| author | Oh, Jihwan Choi, Jeonghwan Kim, Nicole Hee-Yeon Yun, Taewon Song, Hwanjun |
| author_facet | Oh, Jihwan Choi, Jeonghwan Kim, Nicole Hee-Yeon Yun, Taewon Song, Hwanjun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning to Verify Summary Facts with Fine-Grained LLM Feedback Oh, Jihwan Choi, Jeonghwan Kim, Nicole Hee-Yeon Yun, Taewon Song, Hwanjun Computation and Language Artificial Intelligence 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. |
| title | Learning to Verify Summary Facts with Fine-Grained LLM Feedback |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2412.10689 |