Using Large Language Model to Solve and Explain Physics Word Problems Approaching Human Level

Our work demonstrates that large language model (LLM) pre-trained on texts can not only solve pure math word problems, but also physics word problems-problems to be solved by calculation and inference based on some prior physical knowledge. We collect and annotate the first physics word problem dataset-PhysQA, which contains over 1000 junior high school physics word problems (on Kinematics, Mass&Density, Mechanics, Heat, Electricity). Then we use OpenAI' s GPT3.5 to generate the answer of these problems and found that GPT3.5 could automatically solve 49.3% of the problems on zero-shot learning and 73.2% on few-shot learning. This result show that by using similar problem and its answer as prompt, LLM could solve elementary physics word problems approaching human level. Besides automatically solving problems, GPT3.5 could also summarize the knowledge or topic examined by the problem, generate the relevant explanation, and synthesis new physics word problems according tothe input problems.Our work is the first research on automatically solving, explaining and generating physics word problems of multiple types and scenes, and we gain an acceptable and state-of-art accuracy, which demonstrates the potential of LLM's further application in the field of secondary education.