Summary of Propagation and Pitfalls: Reasoning-based Assessment Of Knowledge Editing Through Counterfactual Tasks, by Wenyue Hua et al.
Propagation and Pitfalls: Reasoning-based Assessment of Knowledge Editing through Counterfactual Tasks
by Wenyue Hua, Jiang Guo, Mingwen Dong, Henghui Zhu, Patrick Ng, Zhiguo Wang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Methodology (stat.ME)
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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 investigates the challenges of propagating updated knowledge within interconnected facts, aiming to improve accurate reasoning. The authors introduce ReCoE, a novel benchmark dataset covering six common reasoning schemes, to analyze existing knowledge editing techniques. They found that current methods, including input augmentation, finetuning, and locate-and-edit, perform poorly on this dataset, particularly in certain reasoning schemes. The analysis highlights the limitations of these methods from a reasoning standpoint, identifying issues with fact-wise editing, fact recall ability, and coherence in generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to keep knowledge up-to-date when it’s connected to other facts. Right now, it’s hard to make sure updates get passed along correctly. The researchers created a new test dataset called ReCoE that shows how different reasoning methods work in real-life situations. They tested existing ways of updating knowledge and found they don’t do very well on this new benchmark. This is because the current methods struggle with making sense of the facts, remembering what’s important, and generating coherent text. |
Keywords
* Artificial intelligence * Recall
