A team of researchers from Jiaotong University, Peking University, and Microsoft have developed a method called LeMa that improves the mathematical reasoning abilities of large language models (LLMs) by teaching them to learn from mistakes. They fine-tune the LLMs using mistake-correction data pairs generated by GPT-4. LeMa consistently improves performance across various LLMs and tasks, achieving higher accuracy in problem-solving.
Learning from Mistakes: Enhancing Mathematical Reasoning in Large Language Models through Error-Driven Learning
Human beings learn and grow from their mistakes, and now, large language models (LLMs) can do the same. LLMs like GPT-3 are trained on vast amounts of data, including examples of correct and incorrect language usage. They understand grammar, syntax, semantics, and even nuances of language use.
Researchers at Jiaotong University, Peking University, and Microsoft have developed LEMA, a method that fine-tunes LLMs using mistake correction data pairs generated by GPT-4. Inspired by the learning process of human students, LEMA generates corrections for incorrect reasoning paths and provides explanations on why they are incorrect and how to arrive at the correct answer.
LEMA consistently improves performance across various LLMs and tasks compared to fine-tuning on correction data alone. For example, LEMA with LLaMA-2-70B achieves 83.5% accuracy on GSM8K and 25.0% accuracy on MATH, while fine-tuning on correction data alone yields 81.4% and 23.6% accuracy, respectively.
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