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Prior over Evidence: Stereotype-Driven Diagnosis in LLM-Based L2 Pronunciation Feedback

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arXiv:2606. 15325v1 Announce Type: new Abstract: Large language models are increasingly deployed for written pronunciation feedback in second-language (L2) English learning, under the assumption that their diagnoses are grounded in the supplied speech evidence rather than in priors from pretraining.

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Authors: Rong Wang, Kun Sun
Released: 2026-06-17 (22d ago)
Topic: Related to current project
Relation to my work: Read next because Prior over Evidence: Stereotype-Driven Diagnosis in LLM-Based L2 Pronunciation Feedback 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, rect, under, alignment, correct, wrong, eval, line. Source: arxiv cs.CL (NLP).
arXiv: 2606.15325

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