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When employing the Socratic method of teaching, instructors guide studentstoward solving a problem on their own rather than providing the solutiondirectly. While this strategy can substantially improve learning outcomes, itis usually time-consuming and cognitively demanding. Automated Socraticconversational agents can augment human instruction and provide the necessaryscale, however their development is hampered by the lack of suitable data fortraining and evaluation. In this paper, we introduce a manually created datasetof multi-turn Socratic advice that is aimed at helping a novice programmer fixbuggy solutions to simple computational problems. The dataset is then used forbenchmarking the Socratic debugging abilities of a number of language models,ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5to zero-shot and chain of thought prompting of the much larger GPT-4. The codeand datasets are made freely available for research at the link below.https://github.com/taisazero/socratic-debugging-benchmark

Can Language Models Employ the Socratic Method? Experiments with Code Debugging