Large Language Model Unlearning for Source Code
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918215622328320 |
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| author | Jiang, Xue Dong, Yihong Zhang, Huangzhao Wang, Tangxinyu Fang, Zheng Ma, Yingwei Cao, Rongyu Li, Binhua Jin, Zhi Jiao, Wenpin Li, Yongbin Li, Ge |
| author_facet | Jiang, Xue Dong, Yihong Zhang, Huangzhao Wang, Tangxinyu Fang, Zheng Ma, Yingwei Cao, Rongyu Li, Binhua Jin, Zhi Jiao, Wenpin Li, Yongbin Li, Ge |
| contents | While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringement, insecure emission, and deprecated API utilization, etc. A straightforward yet promising defense is unlearning, ie., erasing or down-weighting the offending snippets through post-training. However, we find its application to source code often tends to spill over, damaging the basic knowledge of programming languages learned by the LLM and degrading the overall capability. To ease this challenge, we propose PROD for precise source code unlearning. PROD surgically zeroes out the prediction probability of the prohibited tokens, and renormalizes the remaining distribution so that the generated code stays correct. By excising only the targeted snippets, PROD achieves precise forgetting without much degradation of the LLM's overall capability. To facilitate in-depth evaluation against PROD, we establish an unlearning benchmark consisting of three downstream tasks (ie., unlearning of copyrighted code, insecure code, and deprecated APIs), and introduce Pareto Dominance Ratio (PDR) metric, which indicates both the forget quality and the LLM utility. Our comprehensive evaluation demonstrates that PROD achieves superior overall performance between forget quality and model utility compared to existing unlearning approaches across three downstream tasks, while consistently exhibiting improvements when applied to LLMs of varying series. PROD also exhibits superior robustness against adversarial attacks without generating or exposing the data to be forgotten. These results underscore that our approach not only successfully extends the application boundary of unlearning techniques to source code, but also holds significant implications for advancing reliable code generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17125 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model Unlearning for Source Code Jiang, Xue Dong, Yihong Zhang, Huangzhao Wang, Tangxinyu Fang, Zheng Ma, Yingwei Cao, Rongyu Li, Binhua Jin, Zhi Jiao, Wenpin Li, Yongbin Li, Ge Software Engineering While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringement, insecure emission, and deprecated API utilization, etc. A straightforward yet promising defense is unlearning, ie., erasing or down-weighting the offending snippets through post-training. However, we find its application to source code often tends to spill over, damaging the basic knowledge of programming languages learned by the LLM and degrading the overall capability. To ease this challenge, we propose PROD for precise source code unlearning. PROD surgically zeroes out the prediction probability of the prohibited tokens, and renormalizes the remaining distribution so that the generated code stays correct. By excising only the targeted snippets, PROD achieves precise forgetting without much degradation of the LLM's overall capability. To facilitate in-depth evaluation against PROD, we establish an unlearning benchmark consisting of three downstream tasks (ie., unlearning of copyrighted code, insecure code, and deprecated APIs), and introduce Pareto Dominance Ratio (PDR) metric, which indicates both the forget quality and the LLM utility. Our comprehensive evaluation demonstrates that PROD achieves superior overall performance between forget quality and model utility compared to existing unlearning approaches across three downstream tasks, while consistently exhibiting improvements when applied to LLMs of varying series. PROD also exhibits superior robustness against adversarial attacks without generating or exposing the data to be forgotten. These results underscore that our approach not only successfully extends the application boundary of unlearning techniques to source code, but also holds significant implications for advancing reliable code generation. |
| title | Large Language Model Unlearning for Source Code |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2506.17125 |