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Found 2 papers, 1 papers with code
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
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.
Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment
Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets.
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