Summary of Preserving Generalization Of Language Models in Few-shot Continual Relation Extraction, by Quyen Tran et al.
Preserving Generalization of Language models in Few-shot Continual Relation Extraction
by Quyen Tran, Nguyen Xuan Thanh, Nguyen Hoang Anh, Nam Le Hai, Trung Le, Linh Van Ngo, Thien Huu Nguyen
First submitted to arxiv on: 1 Oct 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 research proposes a novel approach to Few-shot Continual Relations Extraction (FCRE), which enables models to learn new relations with limited labeled data while preserving prior knowledge from pre-trained backbones. The method leverages often-discarded language model heads via mutual information maximization, strategically aligning the primary classification head and enhancing model performance. Experimental results demonstrate the efficacy of this approach, highlighting its potential for addressing FCRE challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to help machines learn from small amounts of labeled data while keeping what they already know. The idea is to use extra language processing heads that are often ignored, and teach them to work together with the main classifier head. This helps the model remember what it learned before and improve its performance. The researchers tested this method using large language models and found it worked well. |
Keywords
» Artificial intelligence » Classification » Few shot » Language model
