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Large language models such as Open AI's Generative Pre-trained Transformer(GPT) models are proficient at answering questions, but their knowledge isconfined to the information present in their training data. This limitationrenders them ineffective when confronted with questions about recentdevelopments or non-public documents. Our research proposes a method thatenables GPT models to answer questions by employing context from an informationsource not previously included in their training data. The methodology includespreprocessing of contextual information, the embedding of contexts and queries,constructing prompt through the integration of context embeddings, andgenerating answers using GPT models. We applied this method in a controlledtest scenario using the California Driver's Handbook as the information source.The GPT-3 model achieved a 96% passing score on a set of 50 sample drivingknowledge test questions. In contrast, without context, the model's passingscore fell to 82%. However, the model still fails to answer some questionscorrectly even with providing library of context, highlighting room forimprovement. The research also examined the impact of prompt length and contextformat, on the model's performance. Overall, the study provides insights intothe limitations and potential improvements for GPT models in question-answeringtasks.

Exploring the Effectiveness of GPT Models in Test-Taking: A Case Study of the Driver's License Knowledge Test