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cs.LG2025
Reducing the Probability of Undesirable Outputs in Language Models Using Probabilistic Inference
Stephen Zhao, Aidan Li, Rob Brekelmans +1
Reinforcement learning (RL) has become a predominant technique to align language models (LMs) with human preferences or promote outputs which are deemed to be desirable by a given…
cs.LG2024
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
Nathan Ng, Roger Grosse, Marzyeh Ghassemi
Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts. A minimum description length…
cs.LG2024
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani +1
Numerous capability and safety techniques of Large Language Models (LLMs), including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an…