1 citations · 2 across the 5 of their papers we have counts for
5 papers
Document Retrieval-Aware Chunking (D-RAC): Universal Retrieval-Aware Ingestion of Enterprise Documents via PDF Normalization and Multimodal Markdown Conversion
Uday Allu, Abhivanth Sivaprakash, Pratik Singh +1
Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats -- PDFs, Word documents, presentations, and scans -- whose c…
Web Retrieval-Aware Chunking (W-RAC) for Efficient and Cost-Effective Retrieval-Augmented Generation Systems
Uday Allu, Sonu Kedia, Tanmay Odapally +1
Retrieval-Augmented Generation (RAG) systems critically depend on effective document chunking strategies to balance retrieval quality, latency, and operational cost. Traditional ch…
The Instruction Gap: LLMs get lost in Following Instruction
Vishesh Tripathi, Uday Allu, Biddwan Ahmed
Large Language Models (LLMs) have shown remarkable capabilities in natural language understanding and generation, yet their deployment in enterprise environments reveals a critical…
Vision-Guided Chunking Is All You Need: Enhancing RAG with Multimodal Document Understanding
Vishesh Tripathi, Tanmay Odapally, Indraneel Das +2
Retrieval-Augmented Generation (RAG) systems have revolutionized information retrieval and question answering, but traditional text-based chunking methods struggle with complex doc…
Beyond Extraction: Contextualising Tabular Data for Efficient Summarisation by Language Models
Uday Allu, Biddwan Ahmed, Vishesh Tripathi
The conventional use of the Retrieval-Augmented Generation (RAG) architecture has proven effective for retrieving information from diverse documents. However, challenges arise in h…