FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering

• URL: http://arxiv.org/abs/2503.22985v1
• Date: Sat, 29 Mar 2025 06:20:12 GMT
• Title: FReM: A Flexible Reasoning Mechanism for Balancing Quick and Slow Thinking in Long-Context Question Answering
• Authors: Zhengyi Zhao, Shubo Zhang, Zezhong Wang, Bin Liang, Binyang Li, Kam-Fai Wong,
• Abstract summary: We propose FReM: Flexible Reasoning Mechanism, a method that adjusts reasoning depth according to the complexity of each question.<n>Specifically, FReM leverages synthetic reference QA examples to provide an explicit chain of thought, enabling efficient handling of simple queries.<n>Experiments on seven QA datasets show that FReM improves reasoning accuracy and scalability, particularly for complex multihop questions.
• Score: 18.213334065233465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-context question-answering (LCQA) systems have greatly benefited from the powerful reasoning capabilities of large language models (LLMs), which can be categorized into slow and quick reasoning modes. However, both modes have their limitations. Slow thinking generally leans to explore every possible reasoning path, which leads to heavy overthinking and wastes time. Quick thinking usually relies on pattern matching rather than truly understanding the query logic, which misses proper understanding. To address these issues, we propose FReM: Flexible Reasoning Mechanism, a method that adjusts reasoning depth according to the complexity of each question. Specifically, FReM leverages synthetic reference QA examples to provide an explicit chain of thought, enabling efficient handling of simple queries while allowing deeper reasoning for more complex ones. By doing so, FReM helps quick-thinking models move beyond superficial pattern matching and narrows the reasoning space for slow-thinking models to avoid unnecessary exploration. Experiments on seven QA datasets show that FReM improves reasoning accuracy and scalability, particularly for complex multihop questions, indicating its potential to advance LCQA methodologies.

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