ReverseNER: A Self-Generated Example-Driven Framework for Zero-Shot Named Entity Recognition with Large Language Models

• URL: http://arxiv.org/abs/2411.00533v2
• Date: Mon, 04 Nov 2024 13:15:56 GMT
• Title: ReverseNER: A Self-Generated Example-Driven Framework for Zero-Shot Named Entity Recognition with Large Language Models
• Authors: Anbang Wang,
• Abstract summary: We present ReverseNER, a framework aimed at overcoming the limitations of large language models (LLMs) in zero-shot Named Entity Recognition tasks. Rather than beginning with sentences, this method uses an LLM to generate entities based on their definitions and then expands them into full sentences. This results in well-annotated sentences with clearly labeled entities, while preserving semantic and structural similarity to the task sentences.
• Score: 0.0
• License:
• Abstract: This paper presents ReverseNER, a framework aimed at overcoming the limitations of large language models (LLMs) in zero-shot Named Entity Recognition (NER) tasks, particularly in cases where certain entity types have ambiguous boundaries. ReverseNER tackles this challenge by constructing a reliable example library with the reversed process of NER. Rather than beginning with sentences, this method uses an LLM to generate entities based on their definitions and then expands them into full sentences. During sentence generation, the LLM is guided to replicate the structure of a specific 'feature sentence', extracted from the task sentences by clustering. This results in well-annotated sentences with clearly labeled entities, while preserving semantic and structural similarity to the task sentences. Once the example library is constructed, the method selects the most semantically similar example labels for each task sentence to support the LLM's inference. We also propose an entity-level self-consistency scoring mechanism to improve NER performance with LLMs. Experiments show that ReverseNER significantly outperforms traditional zero-shot NER with LLMs and surpasses several few-shot methods, marking a notable improvement in NER for domains with limited labeled data.

Related papers

• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models [7.714969840571947]
  We introduce the task of generation-based extraction and in-context classification (GEIC) We then propose CascadeNER, a universal and multilingual GEIC framework for few-shot and zero-shot NER. We also introduce AnythingNER, the first NER dataset specifically designed for Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2024-09-17T09:32:12Z)
• Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER [3.4998124138877786]
  We propose SLIMER, an approach designed to tackle never-seen-before entity tags by instructing the model on fewer examples. Experiments demonstrate that definition and guidelines yield better performance, faster and more robust learning.
  arXiv  Detail & Related papers  (2024-07-01T13:25:33Z)
• llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models [1.1196013962698619]
  This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with large language models (LLMs) llmNER can compose prompts, query the model, and parse the completion returned by the LLM. We validated our software on two NER tasks to show the library's flexibility.
  arXiv  Detail & Related papers  (2024-06-06T22:01:59Z)
• In-Context Learning for Few-Shot Nested Named Entity Recognition [53.55310639969833]
  We introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity.
  arXiv  Detail & Related papers  (2024-02-02T06:57:53Z)
• Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models [16.16724411695959]
  This work pushes the performance boundary of zero-shot NER with powerful large language models (LLMs) We propose a training-free self-improving framework, which utilizes an unlabeled corpus to stimulate the self-learning ability of LLMs. Experiments on four benchmarks show substantial performance improvements achieved by our framework.
  arXiv  Detail & Related papers  (2023-11-15T12:47:52Z)
• GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer [4.194768796374315]
  Named Entity Recognition (NER) is essential in various Natural Language Processing (NLP) applications. In this paper, we introduce a compact NER model trained to identify any type of entity. Our model, GLiNER, facilitates parallel entity extraction, an advantage over the slow sequential token generation of Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2023-11-14T20:39:12Z)
• Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach [50.12455129619845]
  Named Entity Recognition (NER) aims to extract and classify entity mentions in the text into pre-defined types. We propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
  arXiv  Detail & Related papers  (2023-09-20T03:15:05Z)
• GPT-NER: Named Entity Recognition via Large Language Models [58.609582116612934]
  GPT-NER transforms the sequence labeling task to a generation task that can be easily adapted by Language Models. We find that GPT-NER exhibits a greater ability in the low-resource and few-shot setups, when the amount of training data is extremely scarce. This demonstrates the capabilities of GPT-NER in real-world NER applications where the number of labeled examples is limited.
  arXiv  Detail & Related papers  (2023-04-20T16:17:26Z)
• CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation [113.99145386490639]
  Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results. We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER. We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
  arXiv  Detail & Related papers  (2022-10-13T13:32:36Z)
• Nested Named Entity Recognition as Holistic Structure Parsing [92.8397338250383]
  This work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all. Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art.
  arXiv  Detail & Related papers  (2022-04-17T12:48:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.