EULER: Fine Tuning a Large Language Model for Socratic Interactions

Using Large Language Models (LLMs) for education triggers numerous challenges. In particular, LLMs are often fine-tuned and instructed for Question Answering tasks. However, such a behavior of directly providing an answer to a prompt does not encourage students to think critically and to self-discover information. In this work, we fine-tune LLMs for Socratic interactions, where a LLM guides students towards discovering answers to their own questions rather than providing a straight answer. We investigate diverse datasets containing various educational materials and Socratic dialogues and show how LLMs can achieve such a behavior with Direct Preference Optimization (DPO). Furthermore, we employ advanced models, such as GPT-4o, to evaluate our models. Our results indicate that DPO can be effectively used to fine-tune LLMs for Socratic dialogue, improving their educational utility.