Paper

Fluency and Faithfulness in Human and Machine Literary Translation

Unreadunread

AI summary

arXiv:2605. 15282v1 Announce Type: new Abstract: Literary translation requires balancing target-language fluency with faithfulness to the source.

Reading status

Priority

About this paper

Authors: Sarah Griebel, Ted Underwood
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Fluency and Faithfulness in Human and Machine Literary Translation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, class, eval, source, control, trained, length, language. Source: arxiv cs.CL (NLP).
arXiv: 2605.15282

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Fluency and Faithfulness in Human and Machine Literary Translation. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.