Parser, Chunking, and Embedding Interactions in Retrieval-Augmented Generation over Indian Government Regulatory Documents

arXiv:2609.31660 · cs.CL, cs.AI, cs.IR · Submitted 2026-09-15 · Read on arXiv

cs.CL, cs.AI, cs.IR

Submitted: 2026-09-15

Updated: 2026-09-15

Code: https://github.com/shubham-61291/pre

Terminology

• Benchmark — 14× in this paper · explained in 2 episodes
• Ablation — 11× in this paper · explained in 1 episode
• RAG — 10× in this paper · explained in 3 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Comparing Chunking and Embedding Strategies for Turkish RAG Systems
• BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
• Ragas: Automated Evaluation of Retrieval Augmented Generation
• RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
• Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion

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