让大模型像人一样多角度思考，提升复杂问题解决能力
Thinking in Many Modes: How Composite Reasoning Elevates Large Language Model Performance with Limited Data
- 引入复合推理机制，动态组合演绎、归纳、溯因等思维模式
- 在医学和科学问答任务中超越现有方法，用更少数据达到更高准确率
- 能根据领域自动选择最优推理方式，适合高难度问题求解场景
大型语言模型尽管能力强大，却往往依赖单一的主导推理范式，限制了其在复杂问题上的表现。为此，我们提出复合推理（Composite Reasoning, CR），一种新推理方法，使大模型能够动态探索并组合多种推理风格，如演绎、归纳和溯因，实现更精细的问题求解。在科学与医学问答基准测试中，该方法优于链式思维（CoT）等现有基线，也超过DeepSeek-R1类推理（SR）性能，同时展现出更强的样本效率与合理的令牌使用量。值得注意的是，CR会自适应强调符合领域的推理风格：在医学问答中优先采用溯因与演绎，在科学推理中则转向因果、演绎与归纳方法。研究结果表明，通过培养内部推理风格多样性，大模型可获得更鲁棒、自适应且高效的解决问题能力。
原文摘要 · Abstract (English)
Large Language Models (LLMs), despite their remarkable capabilities, rely on singular, pre-dominant reasoning paradigms, hindering their performance on intricate problems that demand diverse cognitive strategies. To address this, we introduce Composite Reasoning (CR), a novel reasoning approach empowering LLMs to dynamically explore and combine multiple reasoning styles like deductive, inductive, and abductive for more nuanced problem-solving. Evaluated on scientific and medical question-answering benchmarks, our approach outperforms existing baselines like Chain-of-Thought (CoT) and also surpasses the accuracy of DeepSeek-R1 style reasoning (SR) capabilities, while demonstrating superior sample efficiency and adequate token usage. Notably, CR adaptively emphasizes domain-appropriate reasoning styles. It prioritizes abductive and deductive reasoning for medical question answering, but shifts to causal, deductive, and inductive methods for scientific reasoning. Our findings highlight that by cultivating internal reasoning style diversity, LLMs acquire more robust, adaptive, and efficient problem-solving abilities.
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