Explanations from Large Language Models Make Small Reasoners Better
Project Overview
The document examines the application of generative AI, specifically large language models (LLMs), in enhancing educational tools by improving the performance of smaller language models (SLMs) through the integration of generated explanations. It focuses on multi-task learning frameworks that utilize these explanations to advance reasoning skills, particularly in question-answering scenarios within educational settings. Key findings reveal that incorporating explanations generated by LLMs can lead to significant performance improvements in SLMs, sometimes even exceeding the capabilities of larger models in specific tasks. This underscores the critical role of explainable AI in education, as the generation of high-quality explanations not only boosts model effectiveness but also provides valuable insights for learners. Overall, the document highlights the transformative potential of generative AI in educational contexts, emphasizing its ability to refine learning tools and enhance students' understanding through improved communication and reasoning.
Key Applications
Multi-task Learning with Explanations from LLM
Context: Educational settings, targeting students and researchers in AI and NLP.
Implementation: Utilizing explanations generated by LLMs in a multi-task learning framework to train SLMs on reasoning tasks.
Outcomes: SLMs show significant performance improvements on reasoning tasks compared to single-task fine-tuning baselines.
Challenges: Requires extensive tuning of hyperparameters and may struggle with noisy explanations.
Implementation Barriers
Technical Barrier
Integration of explanation generation into training frameworks requires careful tuning and optimization.
Proposed Solutions: Use multi-task learning strategies to balance between prediction accuracy and explanation quality.
Quality Barrier
Generated explanations may sometimes be logically inconsistent or grammatically incorrect.
Proposed Solutions: Implement filtering mechanisms or combine multiple explanation generation strategies to enhance quality.
Project Team
Shiyang Li
Researcher
Jianshu Chen
Researcher
Yelong Shen
Researcher
Zhiyu Chen
Researcher
Xinlu Zhang
Researcher
Zekun Li
Researcher
Hong Wang
Researcher
Jing Qian
Researcher
Baolin Peng
Researcher
Yi Mao
Researcher
Wenhu Chen
Researcher
Xifeng Yan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, Wenhu Chen, Xifeng Yan
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai