AI RESEARCH
Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting
arXiv CS.CL
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ArXi:2510.17210v3 Announce Type: replace The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Along with this, the potential retention of sensitive data of LLMs has spurred increasing research into machine unlearning. However, existing unlearning approaches face a critical dilemma: Aggressive unlearning compromises model utility, while conservative strategies preserve utility but risk hallucinated responses. This significantly limits LLMs' reliability in knowledge-intensive applications.