Summary of Cllmfs: a Contrastive Learning Enhanced Large Language Model Framework For Few-shot Named Entity Recognition, by Yafeng Zhang et al.
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition
by Yafeng Zhang, Zilan Yu, Yuang Huang, Jing Tang
First submitted to arxiv on: 23 Aug 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 A novel framework called CLLMFS is proposed for Few-Shot Named Entity Recognition (NER), which enhances a pre-trained Large Language Model (LLM) with Contrastive Learning and Low-Rank Adaptation (LoRA). This approach improves both entity boundary awareness and recognition accuracy, achieving state-of-the-art performance improvements on F1-score ranging from 2.58% to 97.74% across several recognized benchmarks. The CLLMFS framework demonstrates robust generalization capabilities through cross-domain NER experiments conducted on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Few-shot NER is a significant task in natural language processing that involves identifying named entities with only limited labeled data. Existing methods, such as enriching label semantics and employing metric learning techniques, have shown some effectiveness but lack robustness across diverse domains due to limited knowledge in their pre-trained models. The proposed CLLMFS framework enhances an LLM’s internal representations through contrastive learning and LoRA, achieving promising results with limited training data. |
Keywords
» Artificial intelligence » F1 score » Few shot » Generalization » Large language model » Lora » Low rank adaptation » Named entity recognition » Natural language processing » Ner » Semantics
