AI systems have shown impressive performance at answering questions by retrieving relevant context. However, with the increasingly large models, it is impossible and often undesirable to constrain models' knowledge or reasoning to only the retrieved context. This leads to a mismatch between the information that these models access to derive the answer and the information available to the user consuming the AI predictions to assess the AI predicted answer. In this work, we study how users interact with AI systems in absence of sufficient information to assess AI predictions. Further, we ask the question of whether adding the requisite background alleviates the concerns around over-reliance in AI predictions. Our study reveals that users rely on AI predictions even in the absence of sufficient information needed to assess its correctness. Providing the relevant background, however, helps users catch AI errors better, reducing over-reliance on incorrect AI predictions. On the flip side, background information also increases users' confidence in their correct as well as incorrect judgments. Contrary to common expectation, aiding a user's perusal of the context and the background through highlights is not helpful in alleviating the issue of over-confidence stemming from availability of more information. Our work aims to highlight the gap between how NLP developers perceive informational need in human-AI interaction and the actual human interaction with the information available to them.

翻译：人工智能系统通过检索相关上下文在回答问题方面表现出色。然而，随着模型规模日益庞大，将模型的知识或推理限制在仅检索到的上下文范围内既不可能也不理想。这导致这些模型获取答案时所依赖的信息，与用户消费人工智能预测以评估其输出时所掌握的信息之间存在错配。本研究探讨了用户在缺乏足够信息评估人工智能预测时如何与之互动。进一步，我们提出疑问：补充必要的背景信息是否能缓解对人工智能预测过度依赖的问题？我们的研究表明，即使在缺乏评估正确性所需信息的情况下，用户仍会依赖人工智能预测。然而，提供相关背景信息有助于用户更好地发现人工智能错误，从而减少对错误预测的过度依赖。另一方面，背景信息也增强了用户对自身正确和错误判断的信心。与普遍预期相反，通过高亮等方式帮助用户浏览上下文和背景信息，并不能有效缓解因信息增加而导致的过度自信问题。本研究旨在揭示自然语言处理开发者对人类-人工智能交互中信息需求的理解，与用户实际利用可用信息进行交互之间的差距。