Language Models as Science Tutors

NLP has recently made exciting progress toward training language models (LMs) with strong scientific problem-solving skills. However, model development has not focused on real-life usecases of LMs for science, including applications in education that require processing long scientific documents. To address this, we introduce TUTOREVAL and TUTORCHAT. TUTOREVAL is a diverse question-answering benchmark consisting of questions about long chapters from STEM textbooks, written by experts. TUTOREVAL helps measure real-life usability of LMs as scientific assistants, and it is the first benchmark combining long contexts, free-form generation, and multidisciplinary scientific knowledge. Moreover, we show that fine-tuning base models with existing dialogue datasets leads to poor performance on TUTOREVAL. Therefore, we create TUTOR-CHAT, a dataset of 80,000 long synthetic dialogues about textbooks. We use TUTORCHAT to fine-tune Llemma models with 7B and 34B parameters. These LM tutors specialized in math have a 32K-token context window, and they excel at TUTOREVAL while performing strongly on GSM8K and MATH. Our datasets build on opensource materials, and we release our models, data, and evaluations publicly. Figure 1: Example from TUTOREVAL. Given the chapter, the student asks a question to the LM Tutor. Both the chapter and the question are fed to the LM Tutor to generate the answer. GPT-4 assesses the generation by referencing the human annotated key points (blue: the tutoring task; yellow: evaluation). See detailed examples in §A.