Summary of Why Does New Knowledge Create Messy Ripple Effects in Llms?, by Jiaxin Qin et al.
Why Does New Knowledge Create Messy Ripple Effects in LLMs?
by Jiaxin Qin, Zixuan Zhang, Chi Han, Manling Li, Pengfei Yu, Heng Ji
First submitted to arxiv on: 2 Jul 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 paper tackles the problem of post-training knowledge editing (KE) for language models (LMs), aiming to ensure accurate and up-to-date knowledge. The authors investigate why most KE methods still create messy ripple effects, where the edited LM struggles to answer logically related knowledge accurately. They identify a salient indicator, GradSim, which measures the cosine similarity between gradients of original facts and their related knowledge. This metric shows a strong positive correlation with ripple effect performance across various LMs, KE methods, and evaluation metrics. The authors also explore three counter-intuitive failure cases (Negation, Over-Ripple, Multi-Lingual) of ripple effects, finding that these failures are often associated with low GradSim values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure language models have the right information. Language models can get old and outdated, so people want to make sure they’re correct. The problem is that when you fix one mistake, it can cause other mistakes to happen. This paper figures out why this happens and finds a way to measure how bad the problem is. They call it GradSim, which measures how similar the new information is to the old information. They found that if GradSim is high, the model is less likely to have problems. This is important because it can help make language models better. |
Keywords
» Artificial intelligence » Cosine similarity
