Summary of Enhancing Relation Extraction Via Supervised Rationale Verification and Feedback, by Yongqi Li et al.
Enhancing Relation Extraction via Supervised Rationale Verification and Feedback
by Yongqi Li, Xin Miao, Shen Zhou, Mayi Xu, Yuyang Ren, Tieyun Qian
First submitted to arxiv on: 10 Dec 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 The proposed automated feedback framework for relation extraction (RE) presents a novel rationale supervisor that verifies rationales and provides re-selected demonstrations as feedback to correct initial predictions. The framework uses causal intervention and observation methods to collect biased/unbiased rationales for contrastive training, followed by a verification-feedback-correction procedure to enhance language models’ capability of handling the RE task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a new way to help large language models do relation extraction better. They made a special “rationale supervisor” that checks why the model is saying something and gives it new examples to learn from. This helps the model get better at doing relation extraction tasks. The team tested their idea and showed that it works much better than other methods. |
