Despite the growing use of large language models (LLMs) for writing tasks, users may hesitate to rely on LLMs when personal style is important. Post-editing LLM-generated drafts or translations is a common collaborative writing strategy, but it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. We conduct a pre-registered online study (n=81) in which participants post-edit LLM-generated drafts for writing tasks where personal style matters to them. Using embedding-based style similarity metrics, we find that post-editing increases stylistic similarity to participants’ unassisted writing and reduces similarity to fully LLM-generated output. However, post-edited text still remains stylistically closer in style to LLM text than to participants’ unassisted control text, and it exhibits reduced stylistic diversity compared to unassisted human text. We find a gap between perceived stylistic authenticity and model-measured stylistic similarity, with post-edited text often perceived as representative of participants’ personal style despite remaining detectable LLM stylistic traces.

Evaluation metrics are an important driver of progress in Machine Translation (MT), but they have been primarily validated on high-resource modern languages. In this paper, we conduct an empirical evaluation of metrics commonly used to evaluate MT from Ancient Chinese into English. Using LLMs, we construct a contrastive test set, pairing high-quality MT and purposefully flawed MT of the same Pre-Qin texts. We then evaluate the ability of each metric to discriminate between accurate and flawed translations.