Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In this paper, we consider the problem of leveraging the explanations generated by LLM to improve the training of small reasoners, which are more favorable in real-production deployment due to their low cost. We systematically explore three explanation generation approaches from LLM and utilize a multi-task learning framework to facilitate small models to acquire strong reasoning power together with explanation generation capabilities. Experiments on multiple reasoning tasks show that our method can consistently and significantly outperform finetuning baselines across different settings, and even perform better than finetuning/prompting a 60x larger GPT-3 (175B) model by up to 9.5% in accuracy. As a side benefit, human evaluation further shows that our method can generate high-quality explanations to justify its predictions, moving towards the goal of explainable AI.
翻译:将自由文本解释与大语言模型(LLM)的内文学习结合起来,可以产生很强的推理能力以及合理的解释。在本文中,我们考虑了利用LLM产生的解释来改进对小理性者的培训的问题,由于成本低,这些小理性者在实际生产部署方面比较有利。我们系统地探讨LLM的三个解释生成方法,并利用多任务学习框架来帮助小模型获得强大的推理力和解释生成能力。对多种推理任务的实验表明,我们的方法可以持续和显著地超越不同环境的精细基准,甚至比微调/促进60x更大的GPT-3(175B)模型的精确度高出9.5%。作为附带好处,人类评估还表明,我们的方法可以产生高质量的解释,证明其预测的合理性,并朝着可解释的AI的目标迈进。