通过适应性因果提示的草图思维去偏Large Language Models
Debiasing Large Language Models via Adaptive Causal Prompting with Sketch-of-Thought
AI总结：
ACPS通过适应性因果提示与草图思维方法，提升Large Language Models的推理效率与泛化能力。
摘要：
尽管在大型语言模型（LLMs）的提示方法上取得了显著进步，例如链式思维（CoT），现有的策略仍然存在过度使用token和在多样化推理任务中泛化能力有限的问题。为了解决这些限制，我们提出了一种适应性因果提示与草图思维（ACPS）框架，该框架利用结构因果模型来推断查询对答案的因果效应，并适应性地选择适当的干预（即标准前门和条件前门调整）。这种设计使能够在异质任务中实现可泛化的因果推理，而无需任务特定的重新训练。通过用简洁的草图思维替代冗长的CoT，ACPS实现了高效的推理，显著减少了token使用和推理成本。在多个推理基准和LLMs上的广泛实验表明，ACPS在准确率、鲁棒性和计算效率方面始终优于现有的提示基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
