Open-source Large Language Models enable projects such as NASA SciX (i.e., NASA ADS) to think out of the box and try alternative approaches for information retrieval and data augmentation, while respecting data copyright and users' privacy. However, when large language models are directly prompted with questions without any context, they are prone to hallucination. At NASA SciX we have developed an experiment where we created semantic vectors for our large collection of abstracts and full-text content, and we designed a prompt system to ask questions using contextual chunks from our system. Based on a non-systematic human evaluation, the experiment shows a lower degree of hallucination and better responses when using Retrieval Augmented Generation. Further exploration is required to design new features and data augmentation processes at NASA SciX that leverages this technology while respecting the high level of trust and quality that the project holds.

翻译：开源大型语言模型使得NASA SciX（即NASA ADS）等科研项目能够突破传统思维，尝试采用创新方法进行信息检索与数据增强，同时兼顾数据版权保护与用户隐私。然而，当大型语言模型在缺乏上下文语境的情况下直接响应提问时，极易产生"幻觉"现象（即生成不准确或虚构的内容）。为此，我们在NASA SciX中设计了一项实验：为海量科研摘要及全文内容构建语义向量，并开发了基于系统上下文片段进行提问的提示系统。基于非系统化的人工评估结果表明，采用检索增强生成（RAG）技术时，模型幻觉程度显著降低且响应质量更优。未来仍需进一步探索如何基于该技术，在坚持项目对可信度与内容质量的严格要求下，为NASA SciX设计新型功能及数据增强处理流程。