mlsanityData to 7 Oct 2026

Augmenting Question Answering with A Hybrid RAG Approach

arXiv 2601.12658 ↗First posted 19 Jan 2026cs.CL cs.AIcited by 0

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) has emerged as a powerful technique for enhancing the quality of responses in Question-Answering (QA) tasks. However, existing approaches often struggle with retrieving contextually relevant information, leading to incomplete or suboptimal answers. In this paper, we introduce Structured-Semantic RAG (SSRAG), a hybrid architecture that enhances QA quality by integrating query augmentation, agentic routing, and a structured retrieval mechanism combining vector and graph based techniques with context unification. By refining retrieval processes and improving contextual grounding, our approach improves both answer accuracy and informativeness. We conduct extensive evaluations on three popular QA datasets, TruthfulQA, SQuAD and WikiQA, across five Large Language Models (LLMs), demonstrating that our proposed approach consistently improves response quality over standard RAG implementations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 42 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

28 of 42 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation17 Sep
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection17 Oct

Other references · 26

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  8. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  9. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  10. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  11. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  12. 2017Billion-scale similarity search with GPUscited by 2,477
14 more
  1. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 889
  2. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  3. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  4. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  5. 2024The Faiss librarycited by 370
  6. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  7. 2024RAFT: Adapting Language Model to Domain Specific RAGcited by 258
  8. 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Modelscited by 174
  9. 2024Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantificationcited by 159
  10. 2024Chain of Thoughtlessness? An Analysis of CoT in Planningcited by 100
  11. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  12. 2024Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Modelscited by 13
  13. 2024Into the Unknown: Self-Learning Large Language Modelscited by 5
  14. 2024Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoningcited by 2

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