::: Amortizing Intractable Inference in Large Language Models

1. using amortized Bayesian inference to sample from these intractable posteriors.
2. fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets).
3. empirical evaluation

presents a new technique for fine-tuning LLMs, to perform amortized inference in probabilistic models