Improving the Validity of Automatically Generated Feedback via
  Reinforcement Learning
Alexander Scarlatos Digory Smith Simon Woodhead Andrew Lan
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.