Pedagogical Alignment of Large Language Models

prompt-engineering
social-sciences
architectures
production
education
TL;DR: Pedagogically-aligned LLMs guide students with feedback, outperforming previous methods in educational settings.
Author

Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, Richard G. Baraniuk

Published

February 7, 2024

Summary:

  • The paper introduces the concept of pedagogically aligned Large Language Models (LLMs) that function as scaffolding tools to guide students through complex problems and provide constructive feedback.
  • The study reinterprets the narrative by viewing the task through the lens of alignment and demonstrates how reinforcement learning through human feedback (RLHF) methods emerge as a superior alternative for aligning LLM behavior.
  • The authors propose a novel approach for constructing a reward dataset specifically designed for the pedagogical alignment of LLMs and apply three state-of-the-art RLHF algorithms, finding that they outperform supervised finetuning (SFT).

Major Findings:

  1. The pedagogically aligned LLMs function as scaffolding tools, breaking complex problems into manageable subproblems and guiding students towards the final answer through constructive feedback and hints.
  2. RLHF methods emerge as a superior alternative for aligning LLM behavior compared to the supervised finetuning approach.
  3. The study demonstrates the effectiveness of reinforcement learning-based alignment algorithms on state-of-the-art LLMs, outperforming the SFT approach significantly.

Analysis and Critique:

  • The paper provides a comprehensive overview of dataset construction, experimental design, and the subsequent findings derived from the application of state-of-the-art RLHF algorithms to train pedagogically-aligned LLMs.
  • The study demonstrates the efficacy of pedagogical alignment on state-of-the-art models and highlights the potential of online feedback for enhancing the performance of pedagogically-aligned LLMs.
  • The authors acknowledge the need for further exploration and refinement of reinforcement learning methods for aligning LLMs with educational needs, indicating potential areas for future research in this domain.

Appendix

Model gpt-3.5-turbo-1106
Date Generated 2024-02-26
Abstract https://arxiv.org/abs/2402.05000v1
HTML https://browse.arxiv.org/html/2402.05000v1
Truncated False
Word Count 5466