mlsanityData to 14 Oct 2026

Scholarly Question Answering using Large Language Models in the NFDI4DataScience Gateway

arXiv 2406.07257 ↗First posted 11 Jun 2024cs.CL cs.AIcited by 2

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

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.

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.

Builds on

8 of 28 references.

References · 8

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2023PaperQA: Retrieval-Augmented Generative Agent for Scientific Researchcited by 149
  6. 2023Leveraging LLMs in Scholarly Knowledge Graph Question Answeringcited by 13
  7. 2023NLQxform: A Language Model-based Question to SPARQL Transformercited by 8
  8. 2020Piveau: A Large-scale Open Data Management Platform based on Semantic Web Technologiescited by 1

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025CG-RAG: Research Question Answering by Citation Graph Retrieval-Augmented LLMscited by 8
  2. 2025The illusion of a perfect metric: Why evaluating AI's words is harder than it lookscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Retrieval-Augmented Generation for Domain-Specific Question Answering: A Case Study on Pittsburgh and CMU
  2. 2024Leveraging Retrieval-Augmented Generation for Persian University Knowledge Retrieval
  3. 2024Aggregated Knowledge Model: Enhancing Domain-Specific QA with Fine-Tuned and Retrieval-Augmented Generation Models
  4. 2025QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
  5. 2024Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems
  6. 2024KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
  7. 2025Answering Students' Questions on Course Forums Using Multiple Chain-of-Thought Reasoning and Finetuning RAG-Enabled LLM
  8. 2026SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering