Search Results for author:
Found 2 papers, 1 papers with code
Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustment
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.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
