Summary of Studying Large Language Model Behaviors Under Context-memory Conflicts with Real Documents, by Evgenii Kortukov et al.
Studying Large Language Model Behaviors Under Context-Memory Conflicts With Real Documents
by Evgenii Kortukov, Alexander Rubinstein, Elisa Nguyen, Seong Joon Oh
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a framework for studying knowledge conflicts in retrieval-augmented generation (RAG), which mitigates issues like temporal degradation and hallucinations in fully parametric language models. By updating incorrect parametric knowledge using real conflicting documents, the authors find that knowledge updates fail less often than previously reported. However, when the model still fails to update its answers, a parametric bias is observed, where the incorrect answer appearing in context makes the knowledge update likelier to fail. This suggests that factual parametric knowledge can negatively influence LLMs’ reading abilities and behaviors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG helps solve problems with language models like temporal degradation, hallucinations, and lack of grounding. The model’s knowledge is updated from documents provided in context, but this can cause conflicts between the model’s knowledge and contextual information. To study these conflicts, previous work created fake documents that contradict the model’s correct answers. This paper presents a new way to look at these conflicts using real documents that contradict the model’s knowledge. The results show that updating the model’s knowledge is less likely to fail when using real documents. However, if the model still makes mistakes, it’s more likely because of its own biases. |
Keywords
» Artificial intelligence » Grounding » Rag » Retrieval augmented generation
