大模型「越想越错」？人大&腾讯团队用信息论揭示：什么时候该想、什么时候别想

Core Insights - The article discusses the inefficiencies in the reasoning capabilities of large models, highlighting the need for a more effective approach to reasoning in AI systems [4][10][46] - The proposed solution, Adaptive Think, allows models to automatically stop reasoning when they reach a sufficient level of confidence, thus improving efficiency and accuracy [7][28][45] Group 1: Inefficiencies in Current Models - Current large models exhibit a tendency to overthink, leading to longer reasoning chains that often result in noise and decreased accuracy [3][19] - Research indicates that longer reasoning chains do not necessarily yield better results, as they can lead to diminishing returns and increased computational costs [19][20][36] - The study employs information theory metrics such as entropy and mutual information to evaluate the reasoning efficiency of models [6][12] Group 2: Adaptive Think Mechanism - The Adaptive Think strategy enables models to self-monitor their reasoning process, terminating when confidence is sufficiently high [28][29] - Experimental results show that Adaptive Think significantly reduces token consumption while maintaining or improving accuracy across various tasks [33][36] - The mechanism allows for dynamic adjustment of reasoning depth based on task difficulty, enhancing both speed and reliability [31][45] Group 3: Experimental Findings - In tests on the GSM8K dataset, Adaptive Think reduced average token usage by over 40% while improving accuracy by 0.93% compared to traditional methods [33] - The approach demonstrated effectiveness across multiple reasoning tasks, with notable improvements in efficiency for common-sense reasoning tasks [36][37] - The findings suggest that many models can achieve correct answers with fewer reasoning steps, challenging the notion that longer reasoning is inherently better [38][46]