Title: Copy-Paste to Mitigate Large Language Model Hallucinations
Primary area: foundation or frontier models, including LLMs
Keywords: RAG Hallucination, Contextual Faithfulness, Model Interpretability, Large Language Model, Knowledge Conflict

Abstract: While Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to generate contextually grounded responses, contextual faithfulness remains challenging as LLMs may not consistently trust provided context, leading to hallucinations that undermine reliability. We observe an inverse correlation between response copying degree and context-unfaithful hallucinations on RAGTruth, suggesting higher copying degrees reduce hallucinations by fostering genuine contextual belief. We propose \textbf{CopyPasteLLM}, obtained through two-stage high-copying response preference training. We design three prompting methods to enhance copying degree, demonstrating that high-copying responses achieve superior contextual faithfulness and hallucination control. These approaches enable a fully automated pipeline that transforms generated responses into high-copying preference data for training CopyPasteLLM. On FaithEval, ConFiQA and PubMedQA, CopyPasteLLM achieves best performance in both counterfactual and original contexts, remarkably with 12.2\% to 24.5\% accuracy improvements on FaithEval over the best baseline, while requiring only 365 training samples—\textit{1/50th} of baseline data. To elucidate CopyPasteLLM's effectiveness, we propose the \textit{Context-Parameter Copying Capturing} algorithm. Interestingly, this reveals that CopyPasteLLM recalibrates reliance on internal parametric knowledge rather than external knowledge during generation.

TL;DR: We propose CopyPasteLLM that trains models to simply copy from context, achieving 12.2-24.5% accuracy improvements with only 365 training samples (1/50th of baseline) and revealing how copy-paste recalibrates parameteric knowledge.