这篇论文发现Transformer学算术的方式跟人很像，用教人算术的策略来调模型，效果还真不错，想了解LLM推理机制和可解释性的话值得一看。
论文在整数算术任务上发现，vanilla Transformer模型可通过人类有效学习方法提升性能。通过将算术任务分解为子任务并做损失收敛顺序分析，揭示LLM学习模式与人类相似，简单子任务学习更快。应用人类问题解决策略和认知赋能方法后，准确率显著提高。研究通过准确率提升实验、可视化验证和解释性分析展示了方法有效性。工作探索了Transformer LLM与人类学习者的潜在相似性，并用可解释AI验证增强信任。
Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies
Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks. However, their subpar performance on seemingly elementary problems, such as basic arithmetic, raises concerns about model reliability, safety, and ethical deployment. In this study, we demonstrate that the performance of a vanilla Transformer model trained on integer arithmetic tasks can be improved using methods effective for human learners. We begin by decomposing the arithmetic task into well-defined subtasks and conducting loss convergence order analysis together with ablation studies for each subtask. Our findings reveal that LLMs exhibit learning patterns similar to those of human learners, with a faster learning speed for simpler subtasks compared to more complex ones. In addition, we successfully improved the accuracy of LLMs by applying problem-solving strategies and cognitive empowerment methods shown to enhance the performance of human learners. This suggests that transformer-based LLMs may share cognitive processes with human learners in arithmetic. Lastly, we provide a comprehensive demonstration of our method's effectiveness, including significant accuracy improvement experiments, visualization verification, and explanation-based analysis to illuminate the intricacies of LLMs in arithmetic learning. In general, this work explores the potential similarities between transformer-based LLMs and human learners, supported by explainable AI (XAI) verifications, ultimately fostering trust in LLMs for critical and high-stakes applications.