Improving the Validity of Automatically Generated Feedback via Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning framework to improve the correctness and pedagogical alignment of automatically generated math feedback using GPT-4 annotations, enhancing feedback quality in educational AI systems.

Contribution

It presents a novel rubric for evaluating math feedback, and a reinforcement learning method that optimizes feedback generation for correctness and pedagogical alignment using GPT-4 annotations.

Findings

Significant improvement in feedback correctness and alignment with Llama 2.

Effective use of GPT-4 for feedback annotation and evaluation.

Qualitative analysis demonstrates enhanced feedback quality.

Abstract

Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platforms has the potential to improve the learning outcomes of many students. However, both feedback generation and evaluation are challenging: feedback content has to be valid especially in subjects like math, which requires models to understand the problem, the solution, and where the student's error lies. Feedback also has to be pedagogically valid to reflect effective tutoring strategies, such as explaining possible misconceptions and encouraging the student, among other desirable features. In this work, we address both problems of automatically generating and evaluating feedback while considering both correctness and alignment. First, we propose a rubric for evaluating math feedback and show that GPT-4 is able to effectively use it to annotate human-written and LLM-generated feedback. Second, we propose a framework for feedback generation that optimizes both correctness and alignment using reinforcement learning (RL). Specifically, we use GPT-4's annotations to create preferences over feedback pairs in an augmented dataset for training via direct preference optimization (DPO). We show that our methods significantly increase the correctness and alignment of generated feedback with Llama 2, an open-source LLM, qualitatively analyze our generation and evaluation systems using case studies, and outline several areas for future work.