CIP: 一种用于在长上下文噪声下缓解幻觉的即插即用因果提示框架
CIP: A Plug-and-Play Causal Prompting Framework for Mitigating Hallucinations under Long-Context Noise
AI总结：
CIP通过因果推理框架提升大语言模型的可解释性、稳定性和效率，有效缓解长上下文噪声下的幻觉问题。
摘要：
大型语言模型在处理长且嘈杂的检索上下文时经常出现幻觉，因为它们依赖于虚假的相关性而不是真正的因果关系。我们提出了CIP，一种轻量级且即插即用的因果提示框架，该框架在输入阶段减轻幻觉。CIP在实体、行动和事件之间构建因果关系序列，并将其注入提示中，以引导推理朝向因果相关证据。通过因果干预和反事实推理，CIP抑制了非因果的推理路径，提高了事实基础和可解释性。在七个主流语言模型上进行的实验，包括GPT-4o、Gemini 2.0 Flash和Llama 3.1，显示CIP一致地提高了推理质量和可靠性，实现了可归因率2.6点的提升，因果一致性分数0.38的提升，以及有效信息密度的四倍增加。API级别分析进一步显示，CIP加速了上下文理解，并将端到端响应延迟减少了高达55.1个百分点。这些结果表明，因果推理可能成为提高大型语言模型可解释性、稳定性和效率的一种有前景的范式。
英文摘要：
Large language models often hallucinate when processing long and noisy retrieval contexts because they rely on spurious correlations rather than genuine causal relationships. We propose CIP, a lightweight and plug-and-play causal prompting framework that mitigates hallucinations at the input stage. CIP constructs a causal relation sequence among entities, actions, and events and injects it into the prompt to guide reasoning toward causally relevant evidence. Through causal intervention and counterfactual reasoning, CIP suppresses non causal reasoning paths, improving factual grounding and interpretability. Experiments across seven mainstream language models, including GPT-4o, Gemini 2.0 Flash, and Llama 3.1, show that CIP consistently enhances reasoning quality and reliability, achieving 2.6 points improvement in Attributable Rate, 0.38 improvement in Causal Consistency Score, and a fourfold increase in effective information density. API level profiling further shows that CIP accelerates contextual understanding and reduces end to end response latency by up to 55.1 percent. These results suggest that causal reasoning may serve as a promising paradigm for improving the explainability, stability, and efficiency of large language models.
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。
