arXiv:2601.21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing methods adopt a suppressive paradigm by modifying parameters, prompts, or neurons associated with biased behavior; however, such approaches are often brittle, weakly generalizable, data-inefficient, and prone to degrading general capability. We propose \textbf{KnowBias}, a lightweight and conceptually distinct framework that mitigates bias by strengthening, rather than suppressing, neurons encoding bia
The proliferation of LLMs into critical applications necessitates robust debiasing methods, and traditional suppressive approaches have shown their limitations, leading researchers to explore novel conceptual frameworks.
Biased LLMs pose significant ethical, social, and economic risks, and effective debiasing is crucial for their safe and equitable deployment across industries, influencing public trust and regulatory acceptance.
The proposed 'KnowBias' framework suggests a paradigm shift from actively suppressing bias to enhancing bias-encoding neurons, potentially offering a more stable and generalizable mitigation strategy compared to existing methods.
- · AI developers
- · Trustworthy AI platforms
- · Industries deploying LLMs
- · Ethical AI research
- · Unmitigated biased AI systems
- · Brittle debiasing methods
- · Organizations reliant on biased outputs
Widespread adoption of 'enhancement' debiasing techniques could lead to more robust and reliable LLMs.
Improved bias mitigation may accelerate LLM integration into sensitive sectors like healthcare and finance, reducing deployment friction.
A conceptual shift in handling AI bias could influence future regulatory frameworks favoring transparency in bias handling over simple suppression.
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Read at arXiv cs.AI