Summary of Making Pre-trained Language Models Better Continual Few-shot Relation Extractors, by Shengkun Ma et al.
Making Pre-trained Language Models Better Continual Few-Shot Relation Extractors
by Shengkun Ma, Jiale Han, Yi Liang, Bo Cheng
First submitted to arxiv on: 24 Feb 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 addresses the challenge of continual few-shot relation extraction, where language models must learn novel relations while avoiding forgetting old ones with limited labeled training data. The main obstacles are catastrophic forgetting and overfitting. To overcome these issues, the authors propose a Contrastive Prompt Learning framework that uses prompt representation to acquire generalized knowledge and margin-based contrastive learning to focus on hard samples. Additionally, they introduce an effective memory augmentation strategy that employs well-crafted prompts to guide ChatGPT in generating diverse samples. The results demonstrate that their method outperforms state-of-the-art methods by a large margin and effectively mitigates catastrophic forgetting and overfitting in low-resource scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models learn new things while remembering old ones. It’s like trying to remember what you did last week, but with words! The big problem is that these models can forget what they learned before if they don’t see the same words again. This paper finds a way to make them better at learning and remembering by using special “prompts” that help them understand what they’re supposed to do. They even have a trick to make sure the model doesn’t get too good at one thing and forget everything else! The results show that this new method is much better than others at doing this tricky task. |
Keywords
» Artificial intelligence » Few shot » Overfitting » Prompt
