Is the Answer in the Text? Challenging ChatGPT with Evidence Retrieval from Instructive Text
Sophie Henning, Talita Anthonio, Wei Long Zhou, Heike Adel, Mohsen Mesgar, Annemarie Friedrich · 2023
Generative language models have recently shown remarkable success in generating answers to questions in a given textual context.However, these answers may suffer from hallucination, wrongly cite evidence, and spread misleading information.In this work, we address this problem by employing ChatGPT, a state-of-the-art generative model, as a machinereading system.We ask it to retrieve answers to lexically varied and open-ended questions from trustworthy instructive texts.We introduce WHERE (WikiHow Evidence REtrieval), a new high-quality evaluation benchmark of a set of WikiHow articles exhaustively annotated with evidence sentences to questions that comes with a special challenge: All questions are about the article's topic, but not all can be answered using the provided context.We interestingly find that when using a regular question-answering prompt, Chat-GPT neglects to detect the unanswerable cases.When provided with a few examples, it learns to better judge whether a text provides answer evidence.Alongside this important finding, our dataset defines a new benchmark for evidence retrieval in question answering, which we argue is one of the necessary next steps for making large language models more trustworthy.