Structured Inference with Large Language Gibbs

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Large Language Gibbs is a novel scheme for structured probabilistic inference that utilizes the knowledge encoded in large language models (LLMs). This method addresses the challenge of accessing LLM knowledge coherently for complex reasoning tasks. Instead of relying on single-pass autoregressive generation, Large Language Gibbs employs an iterative resampling approach. It uses an LLM's conditional distributions as transition operators to resample individual variables, conditioned on others, via the LLM's next-token conditionals. This technique effectively mitigates order-dependent biases and generates a stationary distribution that harmonizes all local conditionals. The approach has been successfully applied to tasks such as sampling from synthetic distributions, consistent reasoning, and Bayesian structure learning, positioning it as a practical alternative to traditional one-pass generation for structured inference.

Key takeaway

For AI Scientists and Machine Learning Engineers tackling structured probabilistic inference, you should consider Large Language Gibbs as a robust alternative to traditional one-pass autoregressive generation. This method offers a way to utilize LLM knowledge for complex reasoning while mitigating order-dependent biases. You can apply this iterative resampling approach to improve consistency in reasoning tasks or for more coherent Bayesian structure learning, potentially yielding more reliable and probabilistically sound results than prior methods.

Key insights

Large Language Gibbs uses LLM conditionals for iterative, bias-free structured probabilistic inference.

Principles

• Iterative resampling avoids order-dependent biases.
• LLM conditionals can serve as MCMC transition operators.
• Stationary distribution reflects local conditional compromise.

Method

Iteratively resample individual variables conditioned on others, using an LLM's next-token conditionals as transition operators in a Gibbs sampling scheme.

In practice

• Apply to consistent reasoning tasks.
• Use for Bayesian structure learning.
• Sample from synthetic distributions.

Topics

• Large Language Models
• Structured Inference
• Gibbs Sampling
• Probabilistic Inference
• Bayesian Structure Learning
• MCMC

Code references

• hyeok9855/large-language-gibbs

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.