Structured Inference with Large Language Gibbs
The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single-pass autoregressive generation, we iteratively resample individual variables conditioned on others using an LLM's next-token conditionals. This approach avoids order-dependent biases and produces a stationary distribution that reflects a ...
| Authors | Sanghyeok Choi, Henry Gouk, Esmeralda S. Whitammer |
|---|---|
| Primary category | cs.LG |
| Published | 17 June 2026 |
| Last updated at source | 2 September 2026 |
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