Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
Quick Take
CAROL (Chain-based Adaptive Reconfiguration Over Lattices) is a new probabilistic framework that reduces hallucinations in large language models by measuring semantic uncertainty. It improves reliability and interpretability in question answering and multi-agent reasoning benchmarks, outperforming likelihood-based and retrieval-augmented methods while ensuring computational efficiency.
Key Points
- CAROL defines semantic uncertainty based on response consistency with trusted context.
- It formulates hallucination mitigation as a Markov chain accept-reject process.
- Empirical results show significant reduction in hallucinations and improved interpretability.
- CAROL maintains competitive computational efficiency compared to existing methods.
Article Excerpt
From source RSS / original summaryarXiv:2605. 27706v1 Announce Type: new 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.
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