Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy

• URL: http://arxiv.org/abs/2402.12821v3
• Date: Fri, 04 Oct 2024 16:07:29 GMT
• Title: Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy
• Authors: Liyan Xu, Zhenlin Su, Mo Yu, Jin Xu, Jinho D. Choi, Jie Zhou, Fei Liu,
• Abstract summary: Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. We consolidate key error types of inconsistent facts in summaries, and incorporate them to facilitate both the zero-shot and supervised paradigms of LLMs.
• Score: 48.29181662640212
• License:
• Abstract: Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorporating task-specific taxonomy into the inference. To this end, we consolidate key error types of inconsistent facts in summaries, and incorporate them to facilitate both the zero-shot and supervised paradigms of LLMs. Extensive experiments on ten datasets of five distinct domains suggest that, zero-shot LLM inference could benefit from the explicit solution space depicted by the error type taxonomy, and achieves state-of-the-art performance overall, surpassing specialized non-LLM baselines, as well as recent LLM baselines. We further distill models that fuse the taxonomy into parameters through our designed prompt completions and supervised training strategies, efficiently substituting state-of-the-art zero-shot inference with much larger LLMs.

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