BLESS: Benchmarking Large Language Models on Sentence Simplification
Tannon Kew Alison Chi Laura Vásquez-Rodríguez Sweta Agrawal Dennis Aumiller Fernando Alva-Manchego Matthew Shardlow
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.