用因果提示减少长文本中的幻觉，提升模型推理准确性。
CIP: A Plug-and-Play Causal Prompting Framework for Mitigating Hallucinations under Long-Context Noise
- 构建实体与事件间的因果序列注入提示，引导模型关注真实因果证据。
- 在7个主流模型上实现2.6分可归因率提升、因果一致性提高0.38、信息密度提升4倍。
- 轻量插件式设计，加速理解并降低55.1%响应延迟，适合高可靠性场景使用。
大语言模型在处理长而嘈杂的检索上下文时容易产生幻觉，因其依赖虚假相关性而非真实因果关系。我们提出CIP，一种轻量级、即插即用的因果提示框架，在输入阶段缓解幻觉问题。CIP构建实体、动作与事件之间的因果关系序列，并将其注入提示，引导推理聚焦于因果相关的证据。通过因果干预和反事实推理，抑制非因果推理路径，增强事实准确性和可解释性。在包括GPT-4o、Gemini 2.0 Flash和Llama 3.1在内的七个主流模型上实验表明，CIP持续提升推理质量与可靠性：可归因率提升2.6分，因果一致性得分提高0.38，有效信息密度增加四倍。API级分析显示，CIP加速上下文理解，端到端响应延迟最高降低55.1%。结果表明，因果推理或可成为提升大模型可解释性、稳定性和效率的有力范式。
原文摘要 · Abstract (English)
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.
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