Paper

Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese

Unreadunread

AI summary

arXiv:2606. 13991v1 Announce Type: new Abstract: The likelihood ratio framework is widely recognized as the logically and legally sound basis for evidential analysis across forensic sciences, and its importance is increasingly acknowledged in analyses of authorship in textual evidence.

Reading status

Priority

About this paper

Authors: Praju Ghatpande, Satoru Tsuge, Shunichi Ishihara, Wataru Zaitsu, Mitsuyuki Inaba
Released: 2026-06-15 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese 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, eval, rate, trained, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.13991

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.