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    <abstract>Large Language Models (LLMs) often exhibit deficiencies with complex reasoning tasks, such as maths, which we attribute to the discrepancy between human reasoning patterns and those presented in the LLMs’ training data. When dealing with complex problems, humans tend to think carefully before expressing solutions. However, they often do not articulate their inner thoughts, including their intentions and chosen methodologies. Consequently, critical insights essential for bridging reasoning steps may be absent in training data collected from human sources. To bridge this gap, we proposes inserting insights between consecutive reasoning steps, which review the status and initiate the next reasoning steps. Unlike prior prompting strategies that rely on a single or a workflow of static prompts to facilitate reasoning, insights are proactively generated to guide reasoning processes. We implement our idea as a reasoning framework, named Thinking Before You Speak (TBYS), and design a pipeline for automatically collecting and filtering in-context examples for the generation of insights, which alleviates human labeling efforts and fine-tuning overheads. Experiments on challenging mathematical datasets verify the effectiveness of TBYS. Project website: https://gitee.com/jswrt/TBYS</abstract>
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%0 Conference Proceedings
%T Thinking Before You Speak: A Proactive Test-time Scaling Approach
%A Liu, Cong
%A Chai, Wenchang
%A Wu, Hejun
%A Pan, Yan
%A Wei, Pengxu
%A Lin, Liang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F liu-etal-2025-thinking
%X Large Language Models (LLMs) often exhibit deficiencies with complex reasoning tasks, such as maths, which we attribute to the discrepancy between human reasoning patterns and those presented in the LLMs’ training data. When dealing with complex problems, humans tend to think carefully before expressing solutions. However, they often do not articulate their inner thoughts, including their intentions and chosen methodologies. Consequently, critical insights essential for bridging reasoning steps may be absent in training data collected from human sources. To bridge this gap, we proposes inserting insights between consecutive reasoning steps, which review the status and initiate the next reasoning steps. Unlike prior prompting strategies that rely on a single or a workflow of static prompts to facilitate reasoning, insights are proactively generated to guide reasoning processes. We implement our idea as a reasoning framework, named Thinking Before You Speak (TBYS), and design a pipeline for automatically collecting and filtering in-context examples for the generation of insights, which alleviates human labeling efforts and fine-tuning overheads. Experiments on challenging mathematical datasets verify the effectiveness of TBYS. Project website: https://gitee.com/jswrt/TBYS
%R 10.18653/v1/2025.findings-emnlp.1341
%U https://aclanthology.org/2025.findings-emnlp.1341/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1341
%P 24643-24650

Markdown (Informal)

[Thinking Before You Speak: A Proactive Test-time Scaling Approach](https://aclanthology.org/2025.findings-emnlp.1341/) (Liu et al., Findings 2025)

• Thinking Before You Speak: A Proactive Test-time Scaling Approach (Liu et al., Findings 2025)

ACL

• Cong Liu, Wenchang Chai, Hejun Wu, Yan Pan, Pengxu Wei, and Liang Lin. 2025. Thinking Before You Speak: A Proactive Test-time Scaling Approach. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 24643–24650, Suzhou, China. Association for Computational Linguistics.