In a four-task benchmark, GPT-4o, Llama3.1-70B, and Gemma2-2B outperform traditional text simplification systems on most automatic metrics, and GPT-4o is preferred over human-written references in a small human study.
BLESS: Benchmarking Large Language Models on Sentence Simplification
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abstract
We present BLESS, a comprehensive performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS). We examine how well off-the-shelf LLMs can solve this challenging task, assessing a total of 44 models, differing in size, architecture, pre-training methods, and accessibility, on three test sets from different domains (Wikipedia, news, and medical) under a few-shot setting. Our analysis considers a suite of automatic metrics as well as a large-scale quantitative investigation into the types of common edit operations performed by the different models. Furthermore, we perform a manual qualitative analysis on a subset of model outputs to better gauge the quality of the generated simplifications. Our evaluation indicates that the best LLMs, despite not being trained on TS, perform comparably with state-of-the-art TS baselines. Additionally, we find that certain LLMs demonstrate a greater range and diversity of edit operations. Our performance benchmark will be available as a resource for the development of future TS methods and evaluation metrics.
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Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification
In a four-task benchmark, GPT-4o, Llama3.1-70B, and Gemma2-2B outperform traditional text simplification systems on most automatic metrics, and GPT-4o is preferred over human-written references in a small human study.