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.
@article{arxiv.2402.12821,
title = {Identifying Factual Inconsistencies in Summaries: Grounding LLM Inference via Task Taxonomy},
author = {Liyan Xu and Zhenlin Su and Mo Yu and Jin Xu and Jinho D. Choi and Jie Zhou and Fei Liu},
journal= {arXiv preprint arXiv:2402.12821},
year = {2024}
}