mlsanityData to 7 Oct 2026

Ancient Wisdom, Modern Tools: Exploring Retrieval-Augmented LLMs for Ancient Indian Philosophy

arXiv 2408.11903 ↗First posted 21 Aug 2024cs.CL cs.CY cs.IRcited by 0

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

Abstract (arXiv)

LLMs have revolutionized the landscape of information retrieval and knowledge dissemination. However, their application in specialized areas is often hindered by factual inaccuracies and hallucinations, especially in long-tail knowledge distributions. We explore the potential of retrieval-augmented generation (RAG) models for long-form question answering (LFQA) in a specialized knowledge domain. We present VedantaNY-10M, a dataset curated from extensive public discourses on the ancient Indian philosophy of Advaita Vedanta. We develop and benchmark a RAG model against a standard, non-RAG LLM, focusing on transcription, retrieval, and generation performance. Human evaluations by computational linguists and domain experts show that the RAG model significantly outperforms the standard model in producing factual and comprehensive responses having fewer hallucinations. In addition, a keyword-based hybrid retriever that emphasizes unique low-frequency terms further improves results. Our study provides insights into effectively integrating modern large language models with ancient knowledge systems. Project page with dataset and code: https://sites.google.com/view/vedantany-10m

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 52 references that are in the map. First posted on arXiv 21 Aug 2024.

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28 of 52 references.

References · 28

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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16 more
  1. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  3. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 484
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  16. 2023Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?cited by 13

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  3. 2025FARSIQA: Faithful and Advanced RAG System for Islamic Question Answering
  4. 2025Retrieval-Augmented Generation: A Comprehensive Survey of Architectures, Enhancements, and Robustness Frontiers
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  7. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  8. 2024On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models