基于大型语言模型用于数学推理的双重指令微调
Dual Instruction Tuning with Large Language Models for Mathematical Reasoning
AI总结：
针对LLMs数学推理中CoT生成易出错的问题，提出结合正向中间推理状态预测与反向指令重构的双重指令微调策略，通过多任务微调有效提升了模型在多种数学推理任务上的准确性与泛化能力。
摘要：
近期进展凸显了利用大型语言模型（LLMs）结合思维链（CoT）数据进行指令微调在数学推理任务上的成功。尽管对LLMs进行了微调，挑战依然存在，例如CoT生成中出现错误、遗漏和冗余步骤，导致答案预测不准确。为缓解此问题，我们提出一种双重指令微调策略，从正向和反向两个方向精心建模数学推理。这涉及引入中间推理状态预测任务（正向推理）和指令重构任务（反向推理），以增强LLMs对指令的理解与执行。这些任务的训练实例基于现有数学指令微调数据集构建。随后，LLMs使用现有数学指令和新创建的数据进行多任务微调。综合实验验证了双重指令微调策略在各种数学推理任务上的有效性与领域泛化能力。
英文摘要：
Recent advancements highlight the success of instruction tuning with large language models (LLMs) utilizing Chain-of-Thought (CoT) data for mathematical reasoning tasks. Despite the fine-tuned LLMs, challenges persist, such as incorrect, missing, and redundant steps in CoT generation leading to inaccuracies in answer predictions. To alleviate this problem, we propose a dual instruction tuning strategy to meticulously model mathematical reasoning from both forward and reverse directions. This involves introducing the Intermediate Reasoning State Prediction task (forward reasoning) and the Instruction Reconstruction task (reverse reasoning) to enhance the LLMs' understanding and execution of instructions. Training instances for these tasks are constructed based on existing mathematical instruction tuning datasets. Subsequently, LLMs undergo multi-task fine-tuning using both existing mathematical instructions and the newly created data. Comprehensive experiments validate the effectiveness and domain generalization of the dual instruction tuning strategy across various mathematical reasoning tasks.
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