@inproceedings{li-etal-2026-debiasing,
title = "Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought",
author = "Li, Bowen and
Xu, Ziqi and
Ren, Jing and
Luo, Renqiang and
Zhang, Xikun and
Zhang, Xiuzhen and
Ren, Yongli and
Xia, Feng",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.234/",
pages = "4481--4499",
ISBN = "979-8-89176-386-9",
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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<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.</abstract>
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%0 Conference Proceedings
%T Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
%A Li, Bowen
%A Xu, Ziqi
%A Ren, Jing
%A Luo, Renqiang
%A Zhang, Xikun
%A Zhang, Xiuzhen
%A Ren, Yongli
%A Xia, Feng
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F li-etal-2026-debiasing
%X 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.
%U https://aclanthology.org/2026.findings-eacl.234/
%P 4481-4499
Markdown (Informal)
[Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought](https://aclanthology.org/2026.findings-eacl.234/) (Li et al., Findings 2026)
ACL
- Bowen Li, Ziqi Xu, Jing Ren, Renqiang Luo, Xikun Zhang, Xiuzhen Zhang, Yongli Ren, and Feng Xia. 2026. Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought. In Findings of the Association for Computational Linguistics: EACL 2026, pages 4481–4499, Rabat, Morocco. Association for Computational Linguistics.