Summary of Adaptive Prompting For Continual Relation Extraction: a Within-task Variance Perspective, by Minh Le et al.
Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
by Minh Le, Tien Ngoc Luu, An Nguyen The, Thanh-Thien Le, Trang Nguyen, Tung Thanh Nguyen, Linh Ngo Van, Thien Huu Nguyen
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 research proposes a novel approach to address catastrophic forgetting in Continual Relation Extraction (CRE) by employing a prompt pool for each task and incorporating a generative model to consolidate prior knowledge. The method aims to overcome limitations of existing prompt-based approaches, including inaccurate prompt selection, inadequate mechanisms for mitigating forgetting, and suboptimal handling of cross-task and within-task variances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The approach is designed to capture variations within each task while enhancing cross-task variances, eliminating the need for explicit data storage. The method demonstrates superior performance over state-of-the-art prompt-based and rehearsal-free methods in CRE tasks. This research has important implications for natural language processing and machine learning applications that require continual learning. |
Keywords
» Artificial intelligence » Continual learning » Generative model » Machine learning » Natural language processing » Prompt
