Experimenting with Large Language Models and vector embeddings in NASA SciX
This addresses information retrieval challenges for NASA SciX users, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of hallucination in large language models for information retrieval at NASA SciX by using Retrieval Augmented Generation with semantic vectors, resulting in lower hallucination and better responses based on non-systematic human evaluation.
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.