Summary of Baner: Boundary-aware Llms For Few-shot Named Entity Recognition, by Quanjiang Guo et al.
BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
by Quanjiang Guo, Yihong Dong, Ling Tian, Zhao Kang, Yu Zhang, Sijie Wang
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper addresses the challenges of few-shot named entity recognition (NER) in prototypical networks, specifically over/under-detected false spans and unaligned entity prototypes. The authors propose Boundary-Aware LLMs for Few-Shot Named Entity Recognition to enhance the perception of entity boundaries and improve cross-domain classification capabilities using LoRAHub. The framework outperforms prior methods on various benchmarks, demonstrating its effectiveness across different LLM architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in machine learning. Right now, it’s hard to recognize named entities (like people or places) from text when we only have a little bit of information about them. The authors came up with a new way to do this using special kinds of computer models called LLMs. They made the model better by making it pay attention to where the entity boundaries are, which helps it recognize things more accurately. This is important because it can help computers understand text better and make decisions based on that understanding. |
Keywords
» Artificial intelligence » Attention » Classification » Few shot » Machine learning » Named entity recognition » Ner
