CITING: Large Language Models Create Curriculum for Instruction Tuning
Project Overview
The document explores the innovative approach of Curriculum Instruction Tuning (CITING), which utilizes large language models (LLMs) to improve instructional tuning in education. By employing AI to generate rubrics and deliver feedback, CITING replicates the tutoring process, enhancing the learning experience for students. This method significantly outperforms conventional techniques such as reinforcement learning from human feedback (RLHF) by producing more articulated, in-depth, and comprehensive responses across multiple datasets. The findings underscore the potential of generative AI in education, highlighting its capability to refine instructional methods and improve student outcomes through tailored feedback and enhanced learning materials. Overall, the implementation of CITING exemplifies a transformative application of generative AI, demonstrating its effectiveness in fostering deeper learning and engagement in educational settings.
Key Applications
Curriculum Instruction Tuning (CITING)
Context: Educational context involves training student language models using AI-generated curricula, targeting educators, AI researchers, and language model developers.
Implementation: Implemented through a two-step process where a teacher LLM creates rubrics for evaluating student responses, and the student LLM learns to improve its outputs based on these criteria and AI feedback.
Outcomes: Achieved substantial improvements in response quality, outperforming traditional methods in articulation, depth, and comprehensiveness.
Challenges: Challenges include reliance on high-quality LLM-generated data, potential hallucinations in outputs, and computational costs associated with training and aligning models.
Implementation Barriers
Technical
Building high-quality instruction datasets and aligning LLMs with human feedback is labor-intensive and time-consuming. Additionally, synthetic data generated by LLMs can contain hallucinations, leading to suboptimal performance.
Proposed Solutions: Utilizing AI models to generate instruction data and provide feedback to reduce human effort and improve efficiency. Employing methods to refine the synthetic instruction tuning process by prompting teacher LLMs for multi-step reasoning.
Computational
Reinforcement learning methods are sensitive to hyperparameters and can be computationally expensive.
Proposed Solutions: Optimize training processes and use efficient algorithms to mitigate resource demands.
Project Team
Tao Feng
Researcher
Zifeng Wang
Researcher
Jimeng Sun
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Tao Feng, Zifeng Wang, Jimeng Sun
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