Summary of Investigating Context-faithfulness in Large Language Models: the Roles Of Memory Strength and Evidence Style, by Yuepei Li et al.
Investigating Context-Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style
by Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates how Large Language Models (LLMs) incorporate external information into their response generation process, also known as retrieval-augmented generation (RAG). The authors focus on the impact of memory strength and evidence presentation on LLMs’ context-faithfulness. They introduce a novel method to quantify LLMs’ memory strength by measuring responses to different paraphrases of the same question. Two datasets are used for evaluation: Natural Questions (NQ) with popular questions and popQA featuring long-tail questions. The results show that larger LLMs like GPT-4 rely more on internal memory when questions have high memory strength, while presenting paraphrased evidence increases receptiveness to external information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how language models use information from the world to answer questions. It looks at how well these models remember things and how they use new information to improve their answers. The researchers created a way to measure how strong a model’s memory is by testing its responses to different versions of the same question. They used two types of questions: popular ones and longer, more specific ones. Their results show that bigger language models are better at remembering things when given complex questions, but they can still learn from new information. |
Keywords
» Artificial intelligence » Gpt » Rag » Retrieval augmented generation
