arXiv:2605.30919v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many existing LLM unlearning approaches rely on optimizing prediction loss(es), such as maximizing the loss on the forget set, but often face critical issues like over-forgetting and poor model utility. To address them, this paper novelly frames the optimization objective for LLM unlearning as one of zeroing out data attribution instead. In particular, we propose the first
The rapid development and public deployment of large language models (LLMs) have amplified calls for robust unlearning mechanisms to address privacy, regulatory, and ethical concerns, making research into effective solutions particularly timely.
This research introduces a novel and potentially more effective method for LLM unlearning, addressing critical issues like over-forgetting and poor model utility, which are crucial for the responsible and scalable deployment of AI.
The optimization objective for LLM unlearning shifts from loss-maximization to zeroing out data attribution, potentially leading to more precise and less destructive unlearning processes.
- · AI developers
- · Organizations deploying LLMs
- · Privacy advocates
- · Regulatory bodies
- · Malicious actors
- · Models trained on problematic data
Improved LLM unlearning techniques enable more ethical and compliant AI systems.
Enhanced trust in AI allows for broader adoption and integration into sensitive applications.
New regulatory frameworks may emerge, mandating data attribution-based unlearning, further accelerating its development and deployment.
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