Summary of Knowledge Conflicts For Llms: a Survey, by Rongwu Xu et al.
Knowledge Conflicts for LLMs: A Survey
by Rongwu Xu, Zehan Qi, Zhijiang Guo, Cunxiang Wang, Hongru Wang, Yue Zhang, Wei Xu
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 survey provides an in-depth analysis of knowledge conflicts for large language models (LLMs), highlighting complex challenges when blending contextual and parametric knowledge. It categorizes three types of knowledge conflicts: context-memory, inter-context, and intra-memory conflict, which can impact the trustworthiness and performance of LLMs. The paper aims to improve the robustness of LLMs by shedding light on strategies for addressing these conflicts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are super smart computer programs that can understand and generate human-like text. But sometimes they get confused when trying to use information from different contexts or memories. This paper looks at what happens when this happens, why it’s a problem, and how we can make LLMs better at handling these situations. |
