用马尔可夫链追踪上下文信息流失，减少大模型幻觉。
Maintaining Informative Coherence: Migrating Hallucinations in Large Language Models via Absorbing Markov Chains
- 基于吸收马尔可夫链量化上下文重要性，评估信息丢失程度。
- 在TruthfulQA等数据集上显著降低幻觉率，无需额外训练。
- 适合需要高可信度生成的网页应用与内容审核场景。
大型语言模型在文本生成、翻译和摘要中表现强大，但常因采样策略及训练数据偏差导致幻觉——即生成过程中无法保持上下文一致性，忽略关键细节。这些错误可能在网络中传播，影响信息可信度。本文提出一种新型解码策略，利用吸收马尔可夫链分析从首词到末词的所有可能路径，量化上下文信息的重要性并测量信息损失程度。该方法无需额外训练或外部数据，即可提升输出可靠性。在TruthfulQA、FACTOR和HaluEval等数据集上的评估表明，该方法能有效缓解幻觉问题，凸显了保障网络应用中信息准确流动的重要性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are powerful tools for text generation, translation, and summarization, but they often suffer from hallucinations-instances where they fail to maintain the fidelity and coherence of contextual information during decoding, sometimes overlooking critical details due to their sampling strategies and inherent biases from training data and fine-tuning discrepancies. These hallucinations can propagate through the web, affecting the trustworthiness of information disseminated online. To address this issue, we propose a novel decoding strategy that leverages absorbing Markov chains to quantify the significance of contextual information and measure the extent of information loss during generation. By considering all possible paths from the first to the last token, our approach enhances the reliability of model outputs without requiring additional training or external data. Evaluations on datasets including TruthfulQA, FACTOR, and HaluEval highlight the superior performance of our method in mitigating hallucinations, underscoring the necessity of ensuring accurate information flow in web-based applications.
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