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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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