# Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

arXiv: https://arxiv.org/abs/2406.07257 · HTML: https://mlsanity.com/paper/2406.07257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [Leveraging LLMs in Scholarly Knowledge Graph Question Answering](https://mlsanity.com/paper/2311.09841/) (2023)
- [NLQxform: A Language Model-based Question to SPARQL Transformer](https://mlsanity.com/paper/2311.07588/) (2023)
- [Piveau: A Large-scale Open Data Management Platform based on Semantic Web Technologies](https://mlsanity.com/paper/2005.02614/) (2020)

## Built on by (most cited first, 2 of 2)

- [CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMs](https://mlsanity.com/paper/2501.15067/) (2025)
- [The illusion of a perfect metric: Why evaluating AI's words is harder than it looks](https://mlsanity.com/paper/2508.13816/) (2025)

## Abstract (arXiv)

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.