Assessing the efficacy of large language models in generating accurate teacher responses

TL;DR

This study evaluates large language models' ability to generate accurate, pedagogically valuable teacher responses in educational dialogues, highlighting GPT-4's superior performance and challenges in fine-tuning for educational purposes.

Contribution

It provides an extensive comparison of generative models for educational dialogue, including GPT-4, GPT-2, DialoGPT, and Flan-T5, and discusses challenges in fine-tuning for pedagogical quality.

Findings

GPT-4 outperforms other models in generating educational responses

Fine-tuned models face challenges due to dataset characteristics

Current evaluation metrics may not fully capture pedagogical skills

Abstract

(Tack et al., 2023) organized the shared task hosted by the 18th Workshop on Innovative Use of NLP for Building Educational Applications on generation of teacher language in educational dialogues. Following the structure of the shared task, in this study, we attempt to assess the generative abilities of large language models in providing informative and helpful insights to students, thereby simulating the role of a knowledgeable teacher. To this end, we present an extensive evaluation of several benchmarking generative models, including GPT-4 (few-shot, in-context learning), fine-tuned GPT-2, and fine-tuned DialoGPT. Additionally, to optimize for pedagogical quality, we fine-tuned the Flan-T5 model using reinforcement learning. Our experimental findings on the Teacher-Student Chatroom Corpus subset indicate the efficacy of GPT-4 over other fine-tuned models, measured using BERTScore and DialogRPT. We hypothesize that several dataset characteristics, including sampling, representativeness, and dialog completeness, pose significant challenges to fine-tuning, thus contributing to the poor generalizability of the fine-tuned models. Finally, we note the need for these generative models to be evaluated with a metric that relies not only on dialog coherence and matched language modeling distribution but also on the model's ability to showcase pedagogical skills.