Large language models (LLMs) are prone to hallucinations, i.e., nonsensical, unfaithful, and undesirable text. Users tend to overrely on LLMs and corresponding hallucinations which can lead to misinterpretations and errors. To tackle the problem of overreliance, we propose HILL, the "Hallucination Identifier for Large Language Models". First, we identified design features for HILL with a Wizard of Oz approach with nine participants. Subsequently, we implemented HILL based on the identified design features and evaluated HILL's interface design by surveying 17 participants. Further, we investigated HILL's functionality to identify hallucinations based on an existing question-answering dataset and five user interviews. We find that HILL can correctly identify and highlight hallucinations in LLM responses which enables users to handle LLM responses with more caution. With that, we propose an easy-to-implement adaptation to existing LLMs and demonstrate the relevance of user-centered designs of AI artifacts.

翻译：大型语言模型（LLM）容易产生幻觉，即生成无意义、不忠实且不合意文本。用户往往过度依赖LLM及其产生的幻觉，这可能导致误解和错误。为解决过度依赖问题，我们提出HILL——"面向大型语言模型的幻觉识别器"。首先，我们采用"绿野仙踪"方法（Wizard of Oz approach），通过九名参与者确定了HILL的设计特征。随后基于这些设计特征实现HILL，并通过对17名参与者的问卷调查评估了HILL的界面设计。进一步地，我们基于现有问答数据集和五次用户访谈研究了HILL识别幻觉的功能。研究表明，HILL能够正确识别并高亮标注LLM响应中的幻觉，从而使用户更加审慎地处理LLM输出。据此，我们提出一种易于实现的现有LLM适配方案，并论证了以用户为中心设计AI工件的重要性。