llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929377064779776 |
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| author | Villena, Fabián Miranda, Luis Aracena, Claudio |
| author_facet | Villena, Fabián Miranda, Luis Aracena, Claudio |
| contents | Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library's flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_04528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models Villena, Fabián Miranda, Luis Aracena, Claudio Computation and Language Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple variables. We validated our software on two NER tasks to show the library's flexibility. llmNER aims to push the boundaries of in-context learning research by removing the barrier of the prompting and parsing steps. |
| title | llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.04528 |