Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style
This addresses the problem of improving context-faithfulness in LLMs for developers and researchers, though it is incremental as it builds on existing RAG methods.
The study investigated how memory strength and evidence presentation affect the context-faithfulness of Large Language Models (LLMs) in retrieval-augmented generation, finding that LLMs rely more on internal memory for high-memory-strength questions and paraphrased evidence increases receptiveness.
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 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 examine LLMs' behavior. Our results show that for questions with high memory strength, LLMs are more likely to rely on internal memory. Furthermore, presenting paraphrased evidence significantly increases LLMs' receptiveness compared to simple repetition or adding details. These findings provide key insights for improving retrieval-augmented generation and context-aware LLMs. Our code is available at https://github.com/liyp0095/ContextFaithful.