探究大型语言模型在多语言和多领域设置中的复杂词识别
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups
AI总结：
本研究探究大型语言模型（如Llama、GPT）在复杂词识别、词汇复杂度预测及多词表达评估中的表现，发现其在零样本、少样本及微调条件下难以超越现有较小模型，并探讨了元学习与提示学习的结合。
摘要：
复杂词识别（CWI）是词汇简化任务中的一个关键步骤，并且最近已成为一个独立的任务。这一二分类任务出现了一些变体，如词汇复杂度预测（LCP）和多词表达复杂度评估（MWE）。大型语言模型（LLMs）近年来因其多功能性以及在零样本/少样本设置中解决未见任务的能力而在自然语言处理社区中广受欢迎。我们的工作研究了LLMs的使用，具体包括开源模型如Llama 2、Llama 3和Vicuna v1.5，以及闭源模型如ChatGPT-3.5-turbo和GPT-4o，在CWI、LCP和MWE设置中的应用。我们评估了零样本、少样本和微调设置，并表明LLMs在某些条件下表现不佳或与现有方法相比只能达到相当的结果。此外，我们提供了一些关于元学习与提示学习相结合的观点。最后，我们得出结论，当前LLMs的状态无法或几乎无法超越现有方法，而这些方法通常规模小得多。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
