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Evidence for feature-specific error correction in LLMs

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arXiv:2606. 24964v1 Announce Type: new Abstract: Understanding the features of large language models (LLMs) is a central goal of interpretability.

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Authors: Francisco Ferreira da Silva, Stefan Heimersheim
Released: 2026-06-25 (14d ago)
Topic: Related to current project
Relation to my work: Read next because Evidence for feature-specific error correction in LLMs 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: code, rect, under, correct, control, position, candidate, test. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.24964

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