Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought

• URL: http://arxiv.org/abs/2601.08108v1
• Date: Tue, 13 Jan 2026 00:58:43 GMT
• Title: Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
• Authors: Bowen Li, Ziqi Xu, Jing Ren, Renqiang Luo, Xikun Zhang, Xiuzhen Zhang, Yongli Ren, Feng Xia,
• Abstract summary: We propose an Adaptive Causal Prompting with Sketch-of-Thought (ACPS) framework.<n>ACPS replaces verbose Chain-of-Thought (CoT) with concise Sketch-of-Thought.<n>ACPS consistently outperforms existing prompting baselines in terms of accuracy, robustness, and computational efficiency.
• Score: 18.725256563820952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite notable advancements in prompting methods for Large Language Models (LLMs), such as Chain-of-Thought (CoT), existing strategies still suffer from excessive token usage and limited generalisability across diverse reasoning tasks. To address these limitations, we propose an Adaptive Causal Prompting with Sketch-of-Thought (ACPS) framework, which leverages structural causal models to infer the causal effect of a query on its answer and adaptively select an appropriate intervention (i.e., standard front-door and conditional front-door adjustments). This design enables generalisable causal reasoning across heterogeneous tasks without task-specific retraining. By replacing verbose CoT with concise Sketch-of-Thought, ACPS enables efficient reasoning that significantly reduces token usage and inference cost. Extensive experiments on multiple reasoning benchmarks and LLMs demonstrate that ACPS consistently outperforms existing prompting baselines in terms of accuracy, robustness, and computational efficiency.

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