Optimus: A Robust Defense Framework for Mitigating Toxicity while Fine-Tuning Conversational AI
arXiv:2507.05660v3 Announce Type: replace-cross Abstract: Customizing Large Language Models (LLMs) on untrusted datasets poses severe risks of injecting toxic behaviors. In this work, we introduce Optimus, a novel defense framework designed to mitigate fine-tuning harms while preserving conversational utility. Unlike existing defenses that rely heavily on precise toxicity detection or restrictive filtering, Optimus addresses the critical challenge of ensuring robust mitigation even when toxicity classifiers are imperfect or biased. Optimus integrates a training-free toxicity classification sch
As LLMs become more integrated into critical applications, the problem of fine-tuning on untrusted data and the resultant injection of toxic behaviors has become a pressing technical and ethical challenge.
Ensuring the safety and ethical behavior of AI systems is crucial for their broad adoption and to mitigate risks to individual users and societal norms, directly impacting the trustworthiness and utility of AI.
This framework offers a crucial advancement in making large language models more robust against toxicity during fine-tuning, potentially leading to safer and more deployable AI applications even with imperfect detection.
- · AI developers
- · Enterprises deploying LLMs
- · AI ethics and safety researchers
- · Users of conversational AI
- · Malicious actors attempting to inject toxicity
- · Platforms without robust mitigation strategies
Wider deployment of fine-tuned LLMs in sensitive applications will become more feasible.
Reduced reputational and financial risks for companies deploying AI, accelerating adoption across various sectors.
Enhanced public trust in AI technologies, potentially influencing regulatory approaches towards AI safety.
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Read at arXiv cs.CL