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Dual Instruction Tuning with Large Language Models for Mathematical Reasoning
March 28, 2024, 4:48 a.m. | Yongwei Zhou, Tiejun Zhao
cs.CL updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv challenges cs.cl data highlight language language models large language large language models llms mathematical reasoning predictions reasoning success tasks thought type
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