2 papers
cs.LG2026
Gauss-Newton Unlearning for the LLM Era
Lev McKinney, Anvith Thudi, Juhan Bae +4
Standard large language model training can create models that produce outputs their trainer deems unacceptable in deployment. The probability of these outputs can be reduced using…
cs.AI2025
Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers
Shalev Lifshitz, Sheila A. McIlraith, Yilun Du
By utilizing more computational resources at test-time, large language models (LLMs) can improve without additional training. One common strategy uses verifiers to evaluate candida…