Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway
This work addresses the need for more interactive and efficient access to scientific databases for researchers, though it appears incremental as it applies an existing RAG method to a specific gateway.
The paper tackles the problem of scholarly question answering by introducing a Retrieval Augmented Generation-based system on the NFDI4DataScience Gateway, which enhances filtering and conversational engagement with search results, as demonstrated through experimental analysis.
This paper introduces a scholarly Question Answering (QA) system on top of the NFDI4DataScience Gateway, employing a Retrieval Augmented Generation-based (RAG) approach. The NFDI4DS Gateway, as a foundational framework, offers a unified and intuitive interface for querying various scientific databases using federated search. The RAG-based scholarly QA, powered by a Large Language Model (LLM), facilitates dynamic interaction with search results, enhancing filtering capabilities and fostering a conversational engagement with the Gateway search. The effectiveness of both the Gateway and the scholarly QA system is demonstrated through experimental analysis.