Despite the significant achievements of existing prompting methods such as in-context learning and chain-of-thought for large language models (LLMs), they still face challenges of various biases. Traditional debiasing methods primarily focus on the model training stage, including data augmentation-based and reweight-based approaches, with the limitations of addressing the complex biases of 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 the bias of LLMs. In specific, causal intervention is implemented by designing the prompts without accessing the parameters and logits of LLMs.The chain-of-thoughts generated by LLMs are employed as the mediator variable and the causal effect between the input prompt and the output answers is calculated through front-door adjustment to mitigate model biases. Moreover, to obtain the representation of the samples precisely and estimate the causal effect more accurately, contrastive learning is used to fine-tune the encoder of the samples by aligning the space of the encoder with the LLM. Experimental results show that the proposed causal prompting approach achieves excellent performance on 3 natural language processing datasets on both open-source and closed-source LLMs.
View on arXiv