基于格点链式自适应重配置以减少幻觉
Chain-based Adaptive Reconfiguration Over Lattices for Hallucination Reduction
AI总结：
提出CAROL框架，通过定义语义不确定性度量并在文本序列格点上构建串子模目标，将幻觉缓解转化为马尔可夫链接受-拒绝过程，实现测试时幻觉减少。
摘要：
我们介绍了CAROL（基于格点的链式自适应重配置），一个用于大型语言模型测试时减少幻觉的概率框架。CAROL不依赖于词元级别的不确定性，而是基于生成响应与可信上下文之间的一致性定义了一种语义不确定性度量，在文本序列格点上诱导出一个串子模目标。这种表述使得幻觉缓解可以被建模为一个具有可证明收敛性和接近最优性保证的马尔可夫链接受-拒绝过程，允许模型迭代地优化输出以实现语义一致性。通过在意义层面操作，CAROL将幻觉检测和缓解统一在一个框架内。在问答和多智能体推理基准上的实证结果表明，与基于似然和检索增强的基线相比，CAROL显著减少了幻觉，提高了可靠性和可解释性，同时保持了具有竞争力的计算效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
