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Generative pre-trained language models (GPLMs) like ChatGPT encode in themodel's parameters knowledge the models observe during the pre-training phase.This knowledge is then used at inference to address the task specified by theuser in their prompt. For example, for the question-answering task, the GPLMsleverage the knowledge and linguistic patterns learned at training to producean answer to a user question. Aside from the knowledge encoded in the modelitself, answers produced by GPLMs can also leverage knowledge provided in theprompts. For example, a GPLM can be integrated into a retrieve-then-generateparadigm where a search engine is used to retrieve documents relevant to thequestion; the content of the documents is then transferred to the GPLM via theprompt. In this paper we study the differences in answer correctness generatedby ChatGPT when leveraging the model's knowledge alone vs. in combination withthe prompt knowledge. We study this in the context of consumers seeking healthadvice from the model. Aside from measuring the effectiveness of ChatGPT inthis context, we show that the knowledge passed in the prompt can overturn theknowledge encoded in the model and this is, in our experiments, to thedetriment of answer correctness. This work has important implications for thedevelopment of more robust and transparent question-answering systems based ongenerative pre-trained language models.

Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness