mlsanityData to 14 Oct 2026

Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks

arXiv 2610.20419 ↗First posted 14 Oct 2026cs.CL cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation also fits: RAG robustness and adaptive retrieval

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) models have become almost a cornerstone of open-domain question answering tasks; in which the quality of answers generated in the process, greatly relies on the effectiveness of retrieval mechanisms. In the realm of LLM, the last two years have seen tremendous progress in RAG architectures, with many models reporting to have improved performance through various design innovations. The cultural question and answering systems of South Asia, in particular, present a specific challenge since the region is home to the vast number of languages and traditions, which are closely interrelated. Along with these deeply intertwined historical narratives increase the need for culturally grounded and context-aware interpretation. Despite major architectural advances in LLMs, claiming significant improvements, challenges remain when answering these region specific questions in native languages. In this study, we aim to extract high-performing factors to guide the development of optimized hybrid RAG systems. We further explore whether the integration of RAG changes performance differently for culturally grounded and non-cultural questions in a South Asian multilingual setup, and whether RAG produces a higher overall percentage of correct answers than contexted answering. We also examine how LLM performance changes when RAG methods are integrated into the question-answering pipeline in comparison with non-contexted and contexted answering and, evaluate whether the introduction of RAG benefits popular or non-popular culturally grounded questions more. Not only does our work evaluate the current methods but also carves a path for future designs of more accurate and efficient question-answering pipelines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 19 of its 26 references that are in the map. First posted on arXiv 14 Oct 2026.

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19 of 26 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges8 Aug
  2. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generation11 Mar
  3. 2024CRAG -- Comprehensive RAG Benchmark7 Jun
  4. 2024ARAGOG: Advanced RAG Output Grading1 Apr
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 14

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2025Qwen3 Technical Reportcited by 8,820
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  4. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,386
  5. 2024Gemma: Open Models Based on Gemini Research and Technologycited by 916
  6. 2024Large Language Models: A Surveycited by 525
  7. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
  8. 2025HalluLens: LLM Hallucination Benchmarkcited by 92
  9. 2025Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaborationcited by 20
  10. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 15
  11. 2024Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMscited by 7
  12. 2025Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domaincited by 6
2 more
  1. 2025DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generationcited by 4
  2. 2025From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledgecited by 2

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  1. 2025On the Consistency of Multilingual Context Utilization in Retrieval-Augmented Generation
  2. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  3. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  4. 2024In Defense of RAG in the Era of Long-Context Language Models
  5. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  6. 2025Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task
  7. 2025Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature
  8. 2024Evaluation of RAG Metrics for Question Answering in the Telecom Domain