用检索增强方法高效修正大模型事实错误，无需微调且延迟极低。
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
- 将大模型输出拆解为原子事实，用检索内容逐条验证修正。
- 在两个数据集上相比顶尖基线提升最高达30%的事实准确性。
- 通用性强，适配任意指令微调模型，适合实时系统部署。
大型语言模型在自然语言处理任务中表现优异，但常生成事实性错误内容。本文提出一种低延迟的后置校正方法——检索增强修正（RAC），无需额外微调即可提升大模型的事实性表现。该方法将大模型输出分解为原子事实，利用检索内容进行细粒度验证与修正。大量实验表明，RAC在两个主流事实性评估数据集上相比当前最优基线最高提升30%，验证了其在不同大模型及是否集成检索增强生成（RAG）场景下的有效性与鲁棒性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) exhibit impressive results across a wide range of natural language processing (NLP) tasks, yet they can often produce factually incorrect outputs. This paper introduces a simple but effective low-latency post-correction method, \textbf{Retrieval Augmented Correction (RAC)}, aimed at enhancing the factual performance of LLMs without requiring additional fine-tuning. Our method is general and can be used with any instruction-tuned LLM, and has greatly reduced latency compared to prior approaches. RAC decomposes the LLM's output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the LLM-generated output. Our extensive experiments show that RAC yields up to 30\% improvements over state-of-the-art baselines across two popular factuality evaluation datasets, validating its efficacy and robustness in both with and without the integration of Retrieval-Augmented Generation (RAG) across different LLMs.\footnote{Our code is at \url{https://github.com/jlab-nlp/Retrieval-Augmented-Correction}}
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