New Evaluation Paradigm for Lexical Simplification

• URL: http://arxiv.org/abs/2501.15268v1
• Date: Sat, 25 Jan 2025 16:31:37 GMT
• Title: New Evaluation Paradigm for Lexical Simplification
• Authors: Jipeng Qiang, Minjiang Huang, Yi Zhu, Yunhao Yuan, Chaowei Zhang, Xiaoye Ouyang,
• Abstract summary: Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking. We found large language models (LLMs) can simplify sentences directly with a single prompt, bypassing the traditional pipeline. We propose a new annotation method for constructing an all-in-one LS dataset through human-machine collaboration.
• Score: 15.890439726439276
• License:
• Abstract: Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking, each with separate evaluation datasets. We found large language models (LLMs) can simplify sentences directly with a single prompt, bypassing the traditional pipeline. However, existing LS datasets are not suitable for evaluating these LLM-generated simplified sentences, as they focus on providing substitutes for single complex words without identifying all complex words in a sentence. To address this gap, we propose a new annotation method for constructing an all-in-one LS dataset through human-machine collaboration. Automated methods generate a pool of potential substitutes, which human annotators then assess, suggesting additional alternatives as needed. Additionally, we explore LLM-based methods with single prompts, in-context learning, and chain-of-thought techniques. We introduce a multi-LLMs collaboration approach to simulate each step of the LS task. Experimental results demonstrate that LS based on multi-LLMs approaches significantly outperforms existing baselines.

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