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 [BibTeX] [Marc21]
BLESS: Benchmarking Large Language Models on Sentence Simplification
Type of publication: Conference paper
Citation: Kew_EMNLP2023_2023
Publication status: Accepted
Booktitle: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Year: 2023
Month: December
Location: Singapore
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.
Keywords: evaluation, LLM, NLP, Text Simplification
Projects Idiap
Authors Kew, Tannon
Chi, Alison
Vásquez-Rodríguez, Laura
Agrawal, Sweta
Aumiller, Dennis
Alva-Manchego, Fernando
Shardlow, Matthew
Added by: [UNK]
Total mark: 0
  • Kew_EMNLP2023_2023.pdf