Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo

Explore a comprehensive lecture on probabilistic inference in language models using twisted sequential Monte Carlo methods. Delve into how various techniques for large language models (LLMs) can be framed as sampling from unnormalized target distributions. Learn about the application of Sequential Monte Carlo (SMC) for addressing probabilistic inference challenges in LLMs. Discover the concept of learned twist functions and their role in estimating expected future potential values. Examine a novel contrastive method for learning twist functions and its connections to soft reinforcement learning. Investigate the use of bidirectional SMC bounds for evaluating the accuracy of language model inference techniques. Gain insights into practical applications, including sampling undesirable outputs for harmlessness training, generating reviews with varied sentiment, and performing infilling tasks. Access the related research paper for in-depth understanding of the concepts presented in this 1 hour and 22 minute talk by Rob Brekelmans from Valence Labs.