Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
Abstract: Complex Word Identification (CWI) is an important 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 Llama 2 and ChatGPT 3.5 turbo, in the CWI, LCP, and MWE settings. We show that LLMs may struggle in certain conditions or achieve comparable results against existing methods.
Paper Type: Short
Research Area: NLP Applications
Research Area Keywords: free-text/natural language explanations
Contribution Types: Model analysis & interpretability
Languages Studied: English,German,Spanish
Submission Number: 483
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