Retrieval-augmented generation (RAG) improves Large Language Models (LLMs) by incorporating external information into the response generation process. However, how context-faithful LLMs are and what factors influence LLMs' context-faithfulness remain largely unexplored. In this study, we investigate the impact of memory strength and evidence presentation on LLMs' receptiveness to external evidence. We introduce a method to quantify the memory strength of LLMs by measuring the divergence in LLMs' responses to different paraphrases of the same question, which is not considered by previous works. We also generate evidence in various styles to evaluate the effects of evidence in different styles. Two datasets are used for evaluation: Natural Questions (NQ) with popular questions and popQA featuring long-tail questions. Our results show that for questions with high memory strength, LLMs are more likely to rely on internal memory, particularly for larger LLMs such as GPT-4. On the other hand, presenting paraphrased evidence significantly increases LLMs' receptiveness compared to simple repetition or adding details.

本研究针对大型语言模型（LLMs）在生成响应时的上下文忠实性进行调查，填补了此前研究的 gaps。我们提出了一种量化 LLM 记忆强度的方法，并评估了证据呈现风格对其影响。研究发现，对于高记忆强度的问题，LLMs 更倾向于依赖内部记忆，而呈现不同风格的证据能显著提高其接受度。

大型语言模型中上下文忠实性的研究：记忆强度和证据风格的作用