Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908117669773312 |
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| author | Smădu, Răzvan-Alexandru Ion, David-Gabriel Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia |
| author_facet | Smădu, Răzvan-Alexandru Ion, David-Gabriel Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia |
| contents | Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5-turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01706 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups Smădu, Răzvan-Alexandru Ion, David-Gabriel Cercel, Dumitru-Clementin Pop, Florin Cercel, Mihaela-Claudia Computation and Language Complex Word Identification (CWI) is an essential step in the lexical simplification task and has recently become a task on its own. Some variations of this binary classification task have emerged, such as lexical complexity prediction (LCP) and complexity evaluation of multi-word expressions (MWE). Large language models (LLMs) recently became popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. Our work investigates LLM usage, specifically open-source models such as Llama 2, Llama 3, and Vicuna v1.5, and closed-source, such as ChatGPT-3.5-turbo and GPT-4o, in the CWI, LCP, and MWE settings. We evaluate zero-shot, few-shot, and fine-tuning settings and show that LLMs struggle in certain conditions or achieve comparable results against existing methods. In addition, we provide some views on meta-learning combined with prompt learning. In the end, we conclude that the current state of LLMs cannot or barely outperform existing methods, which are usually much smaller. |
| title | Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.01706 |