arXiv AI September 30, 2026 By Zhengyang Shan, Jiayun Xin, Yanjun Lin, Xu Qian, Zhiang Liu, Minghui Xu, Yue Zhang, Qin Hu, Kun Li, Xiuzhen Cheng

UNBIND: UNlearning By INference-time Directional Steering for Code LLMs

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UNBIND is a code unlearning framework that selectively removes memorized code from large language models at inference time while keeping the model weights unchanged. It constructs separate directional steering for hidden states that correspond to target code, enabling high forgetting rates (97.3–99.1% reduction in target code reproduction) with minimal loss in programming utility. Across multiple baselines, corpora, and evaluation metrics—including F‑BLEU, HumanEval+, and MBPP+—UNBIND consistently achieves the best joint forgetting and utility scores, and it effectively eliminates long exact code spans in repeated extraction tests.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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