  Large language models (e.g., GPT-4) are uniquely capable of producing highly
rated text simplification, yet current human evaluation methods fail to provide
a clear understanding of systems' specific strengths and weaknesses. To address
this limitation, we introduce SALSA, an edit-based human annotation framework
that enables holistic and fine-grained text simplification evaluation. We
develop twenty one linguistically grounded edit types, covering the full
spectrum of success and failure across dimensions of conceptual, syntactic and
lexical simplicity. Using SALSA, we collect 19K edit annotations on 840
simplifications, revealing discrepancies in the distribution of simplification
strategies performed by fine-tuned models, prompted LLMs and humans, and find
GPT-3.5 performs more quality edits than humans, but still exhibits frequent
errors. Using our fine-grained annotations, we develop LENS-SALSA, a
reference-free automatic simplification metric, trained to predict sentence-
and word-level quality simultaneously. Additionally, we introduce word-level
quality estimation for simplification and report promising baseline results.
Our data, new metric, and annotation toolkit are available at
https://salsa-eval.com.
