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Vivek Hebbar

2 accepted papers

2026

Removing Sandbagging in LLMs by Training with Weak Supervision

ICML 2026poster

As AI systems begin to automate complex tasks, supervision increasingly relies on weaker models or limited human oversight that cannot fully verify output quality. A model more capable than its supervisors could exploit this gap through sandbagging, producing work that appears acceptable but falls s…

Cited by 1SourceScholar
2025

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

ICLR 2025poster

As large language models (LLMs) grow more powerful, they also become more difficult to trust. They could be either aligned with human intentions, or exhibit "subversive misalignment" -- introducing subtle errors that bypass safety checks. Although individual errors may not immediately cause harm, ea…

Cited by 3SourcePDFScholar