首次全面评估大模型在四类文本简化任务中的表现
Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
- 对比轻量、闭源、开源大模型与传统方法在四类简化任务上的表现
- 大模型在所有任务中均超越非大模型方法，部分输出优于人工标注参考
- 适合关注大模型在自然语言处理中应用前景的研究者
文本简化（TS）旨在降低文本复杂度的同时保留原意和关键信息。现有研究仅表明大语言模型（LLMs）在句子简化任务上优于监督式非大模型方法。本研究首次对大模型在四个文本简化任务——词汇、句法、句子和文档简化——中的表现进行系统分析。我们通过自动指标与人工评估，比较了轻量级、闭源及开源大模型与传统非大模型方法的性能。实验结果表明，大模型在所有四项任务中均优于非大模型方法，且其生成结果常超过现有人工标注参考的质量。最后，本文提出大模型时代文本简化的未来研究方向。
原文摘要 · Abstract (English)
Text simplification (TS) refers to the process of reducing the complexity of a text while retaining its original meaning and key information. Existing work only shows that large language models (LLMs) have outperformed supervised non-LLM-based methods on sentence simplification. This study offers the first comprehensive analysis of LLM performance across four TS tasks: lexical, syntactic, sentence, and document simplification. We compare lightweight, closed-source and open-source LLMs against traditional non-LLM methods using automatic metrics and human evaluations. Our experiments reveal that LLMs not only outperform non-LLM approaches in all four tasks but also often generate outputs that exceed the quality of existing human-annotated references. Finally, we present some future directions of TS in the era of LLMs.
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