Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction

2026-05-26

AI Summary

Large language models are prone to hallucinations during reasoning, undermining their reliability. This work proposes CAROL, a novel framework that uniquely integrates semantic consistency–driven lattice optimization with Markov chain Monte Carlo (MCMC) resampling. By constructing an uncertainty metric grounded in semantic consistency, CAROL defines a submodular optimization objective over textual lattices and iteratively refines outputs through an accept-reject mechanism, thereby unifying hallucination detection and suppression within a single pipeline. The approach enjoys provable convergence and near-optimality guarantees. Experimental results demonstrate that CAROL significantly reduces hallucination rates in question answering and multi-agent reasoning tasks while enhancing model interpretability, all without sacrificing computational efficiency relative to baseline methods.

Abstract

We introduce CAROL (Chain-based Adaptive Reconfiguration Over Lattices), a probabilistic framework for test-time hallucination reduction in large language models. Rather than relying on token-level uncertainty, CAROL defines a semantic uncertainty measure based on the consistency between generated responses and a trusted context, inducing a string-submodular objective over a lattice of textual sequences. This formulation enables hallucination mitigation to be cast as a Markov chain accept-reject process with provable convergence and near-optimality guarantees, allowing the model to iteratively refine outputs toward semantic consistency. By operating at the level of meaning, CAROL unifies hallucination detection and mitigation within a single framework. Empirical results on question answering and multi-agent reasoning benchmarks show that CAROL significantly reduces hallucinations and improves reliability and interpretability compared to likelihood-based and retrieval-augmented baselines, while maintaining competitive computational efficiency.

Problem

• hallucination reduction
• large language models
• semantic consistency
• test-time adaptation
• trustworthy generation

Innovation

• semantic uncertainty
• string-submodular optimization
• Markov chain reconfiguration
• hallucination reduction
• lattice-based generation