因果提示：基于前门调整的大语言模型提示去偏
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
AI总结：
针对大语言模型提示中的偏见问题，提出基于前门调整的因果提示方法，通过思维链作为中介变量计算因果效应，并在七个NLP数据集上验证了其有效性。
摘要：
尽管现有的提示方法，如上下文学习和思维链，在大语言模型（LLM）上取得了显著进展，但它们仍然面临与各种偏见相关的挑战。传统的去偏方法主要关注模型训练阶段，包括基于数据增强和重新加权的方法，然而它们难以应对LLM中固有的复杂偏见。为了解决这些局限性，我们使用结构因果模型揭示了提示方法背后的因果关系，并提出了一种基于前门调整的新型因果提示方法，以有效缓解LLM的偏见。具体而言，通过设计提示而不访问LLM的参数和对数几率来实现因果干预。将LLM生成的思维链作为中介变量，通过前门调整计算输入提示与输出答案之间的因果效应，以减轻模型偏见。此外，为了准确表示思维链并估计因果效应，使用对比学习对思维链的编码器进行微调，使其空间与LLM的空间对齐。实验结果表明，所提出的因果提示方法在七个自然语言处理数据集上，对开源和闭源LLM均取得了优异的性能。
英文摘要：
Despite the notable advancements of existing prompting methods, such as In-Context Learning and Chain-of-Thought for Large Language Models (LLMs), they still face challenges related to various biases. Traditional debiasing methods primarily focus on the model training stage, including approaches based on data augmentation and reweighting, yet they struggle with the complex biases inherent in LLMs. To address such limitations, the causal relationship behind the prompting methods is uncovered using a structural causal model, and a novel causal prompting method based on front-door adjustment is proposed to effectively mitigate LLMs biases. In specific, causal intervention is achieved by designing the prompts without accessing the parameters and logits of LLMs. The chain-of-thought generated by LLM is employed as the mediator variable and the causal effect between input prompts and output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to accurately represent the chain-of-thoughts and estimate the causal effects, contrastive learning is used to fine-tune the encoder of chain-of-thought by aligning its space with that of the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance across seven natural language processing datasets on both open-source and closed-source LLMs.
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。
