@inproceedings{zhao-etal-2025-t2,
title = "T$^2$: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering",
author = "Zhao, Zhengyi and
Zhang, Shubo and
Wang, Zezhong and
Wang, Huimin and
Zhao, Yutian and
Liang, Bin and
Zheng, Yefeng and
Li, Binyang and
Wong, Kam-Fai and
Wu, Xian",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.185/",
pages = "3731--3756",
ISBN = "979-8-89176-332-6",
abstract = "Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models' inherent reasoning capabilities. To address these limitations, we present T$^2$: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T$^2$ leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T$^2$ works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T$^2$ not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2{\%}."
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<abstract>Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models’ inherent reasoning capabilities. To address these limitations, we present T²: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T² leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T² works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T² not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2%.</abstract>
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%0 Conference Proceedings
%T T²: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering
%A Zhao, Zhengyi
%A Zhang, Shubo
%A Wang, Zezhong
%A Wang, Huimin
%A Zhao, Yutian
%A Liang, Bin
%A Zheng, Yefeng
%A Li, Binyang
%A Wong, Kam-Fai
%A Wu, Xian
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F zhao-etal-2025-t2
%X Recent advances in large language models have demonstrated remarkable performance on Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models’ inherent reasoning capabilities. To address these limitations, we present T²: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T² leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T² works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T² not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2%.
%U https://aclanthology.org/2025.emnlp-main.185/
%P 3731-3756
Markdown (Informal)
[T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering](https://aclanthology.org/2025.emnlp-main.185/) (Zhao et al., EMNLP 2025)
ACL
- Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Huimin Wang, Yutian Zhao, Bin Liang, Yefeng Zheng, Binyang Li, Kam-Fai Wong, and Xian Wu. 2025. T2: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 3731–3756, Suzhou, China. Association for Computational Linguistics.