Summary of Reversener: a Self-generated Example-driven Framework For Zero-shot Named Entity Recognition with Large Language Models, by Anbang Wang et al.
ReverseNER: A Self-Generated Example-Driven Framework for Zero-Shot Named Entity Recognition with Large Language Models
by Anbang Wang, Difei Mei, Zhichao Zhang, Xiuxiu Bai, Ran Yao, Zewen Fang, Min Hu, Zhirui Cao, Haitao Sun, Yifeng Guo, Hongyao Zhou, Yu Guo
First submitted to arxiv on: 1 Nov 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 The paper presents ReverseNER, a method to overcome the limitations of large language models (LLMs) in zero-shot named entity recognition (NER) tasks. It tackles this challenge by generating dozens of entity-labeled sentences through reverse NER processes. The method constructs an example library using LLMs and feature sentences, and then selects semantically similar examples as references for each task sentence to facilitate the LLM’s entity recognition. To improve NER performance with LLMs, the paper also proposes an entity-level self-consistency scoring mechanism. The experiments show that ReverseNER significantly outperforms other zero-shot NER methods with LLMs, marking a notable improvement in NER for domains without labeled data while declining computational resource consumption. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReverseNER is a new way to help computers recognize names and places in text without being trained on the task. This is important because most AI models need lots of training data to learn. The method creates fake sentences that have the same structure as real sentences, but with the names and places already labeled. It uses these fake sentences to teach the computer what names and places look like. Then, it shows the computer some real sentences and asks it to identify the names and places. This way, the computer can learn to recognize names and places without needing lots of training data. |
Keywords
» Artificial intelligence » Named entity recognition » Ner » Zero shot
