arXiv:2605.26454v1 Announce Type: new Abstract: Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-training uses different objectives to shape different behaviors, we argue that unlearning methods should be designed for the language function at issue. To study this, we consider two mechanistically distinct unlearning goals, dangerous-knowledge unlearning and toxicity unlearning. For dangerous knowledge, we introduce a cosine-based, meta-learned variant of RMU. For toxicity, we propose a multi-layer
The increasing deployment and integration of large language models necessitates robust methods for mitigating unintended and harmful behaviors before widespread adoption.
A strategic reader should care because the ability to finely tune or 'unlearn' specific undesirable properties in AI models is crucial for their ethical deployment and public acceptance.
The focus shifts from general unlearning methods to domain-specific objectives, implying a more nuanced and potentially effective approach to AI safety and control.
- · AI safety researchers
- · Organizations deploying LLMs
- · AI governance bodies
- · Ethical AI developers
- · Developers of generic unlearning methods
- · Bad actors exploiting LLMs for harmful content
More sophisticated and targeted unlearning techniques become standard practice in LLM development.
Public trust in AI systems may incrementally improve as models become demonstrably safer and more controllable.
The complexity and cost of developing and maintaining safe LLMs could increase, favoring larger organizations with dedicated safety teams.
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Read at arXiv cs.CL