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 use-cases 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 TUTORCHAT, 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 open-source materials, and we release our models, data, and evaluations publicly.