Aligning Large Language Models through Synthetic Feedback
Project Overview
The document outlines an innovative framework for integrating generative AI, specifically large language models (LLMs), into education by aligning them with human values through synthetic feedback. The proposed approach employs a multi-stage process consisting of reward modeling (RM) with synthetic comparisons, supervised fine-tuning (SFT), and reinforcement learning from synthetic feedback (RLSF), resulting in the development of a model named ALMoST. This model outperforms traditional models trained on human-annotated datasets in alignment benchmarks and receives high preference ratings in human evaluations, indicating its effectiveness in educational contexts. The framework aims to reduce the need for extensive human involvement and reliance on proprietary models, making it a more efficient and accessible solution for implementing AI in educational settings. Overall, the findings suggest that generative AI can significantly enhance learning experiences and outcomes by providing personalized, value-aligned support to students and educators.
Key Applications
Aligned Language Model with Synthetic Training dataset (ALMoST)
Context: Development of a language model that aligns with human values for educational and general use.
Implementation: The model was trained using a three-stage framework involving reward modeling with synthetic feedback, supervised fine-tuning, and reinforcement learning.
Outcomes: ALMoST outperforms existing models like Alpaca and Dolly-v2 in alignment benchmarks, showing a 55-58% preference rate in human evaluations.
Challenges: The necessity for synthetic data generation and potential biases in the model due to limited human interactions.
Implementation Barriers
Human Resource Limitations
Alignment learning traditionally requires significant human effort in creating demonstrations and feedback.
Proposed Solutions: The proposed framework minimizes human labor by using synthetic feedback for training.
Dependence on Proprietary Models
Many existing models rely on proprietary LLMs or extensive human annotations, which can be costly.
Proposed Solutions: The framework introduces a method that does not depend on proprietary models and reduces the need for human annotations.
Project Team
Sungdong Kim
Researcher
Sanghwan Bae
Researcher
Jamin Shin
Researcher
Soyoung Kang
Researcher
Donghyun Kwak
Researcher
Kang Min Yoo
Researcher
Minjoon Seo
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Sungdong Kim, Sanghwan Bae, Jamin Shin, Soyoung Kang, Donghyun Kwak, Kang Min Yoo, Minjoon Seo
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