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Phantom transitions in language model fine-tuning

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arXiv:2606. 07559v1 Announce Type: new Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently.

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Authors: Vaibhav Prakash, Jayasri Dontabhaktuni
Released: 2026-06-09 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Phantom transitions in language model fine-tuning 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, rect, under, correct, soft, token, rate. Source: arxiv cs.CL (NLP).
arXiv: 2606.07559

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