arXiv:2607.03932v1 Announce Type: cross Abstract: LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful concepts, e.g. gender or bio-weapons, present in pre-trained LLMs can lead to unsafe or unethical responses for many such prompts. Removing such undesirable concepts robustly across different prompt types remains a challenging problem, since existing unlearning methods typically ignore the impact of prompt variation. In this paper, we explore a novel adversarial approach
The proliferation of powerful LLMs necessitates robust methods to mitigate bias and harmful content, which is particularly challenging given the diverse ways models are prompted.
Ensuring the ethical and safe deployment of LLMs is critical for broad societal adoption and preventing unintended negative consequences across various applications.
This research provides a more effective approach to unlearning undesirable concepts in LLMs by accounting for prompt variation, moving towards more controllable and safer AI systems.
- · AI developers
- · Ethical AI researchers
- · Industries deploying LLMs
- · Malicious actors
- · Underminers of AI safety
- · Systems with unmitigated biases
More reliable and less biased LLMs become available for enterprise and public use.
Increased trust in AI systems could accelerate their integration into sensitive applications like healthcare and finance.
Reduced regulatory friction for AI deployment as models demonstrate greater safety and ethical compliance.
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Read at arXiv cs.AI