mlsanityData to 14 Oct 2026

RAG-based Question Answering over Heterogeneous Data and Text

arXiv 2412.07420 ↗First posted 10 Dec 2024cs.CL cs.IRcited by 7

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Abstract (arXiv)

This article presents the QUASAR system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pipeline of evidence retrieval followed by answer generation, with the latter powered by a moderate-sized language model. Additionally and uniquely, QUASAR has components for question understanding, to derive crisper input for evidence retrieval, and for re-ranking and filtering the retrieved evidence before feeding the most informative pieces into the answer generation. Experiments with three different benchmarks demonstrate the high answering quality of our approach, being on par with or better than large GPT models, while keeping the computational cost and energy consumption orders of magnitude lower.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 39 of its 43 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

39 of 43 references.

References · 39

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27 more
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  3. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 458
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.

  1. 2025Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunitiescited by 31
  2. 2025Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questionscited by 13
  3. 2026Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Methodcited by 6
  4. 2025Format as a Prior: Quantifying and Analyzing Bias in LLMs for Heterogeneous Datacited by 4
  5. 2025HyFedRAG: A Federated Retrieval-Augmented Generation Framework for Heterogeneous and Privacy-Sensitive Datacited by 4
  6. 2025AutoStyle-TTS: Retrieval-Augmented Generation based Automatic Style Matching Text-to-Speech Synthesiscited by 1
  7. 2025RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QAcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
  2. 2022End-to-End Table Question Answering via Retrieval-Augmented Generation
  3. 2026S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
  4. 2026SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering
  5. 2026When Iterative RAG Beats Ideal Evidence: A Diagnostic Study in Scientific Multi-hop Question Answering
  6. 2025RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
  7. 2026Augmenting Question Answering with A Hybrid RAG Approach
  8. 2025EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes