4 papers
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…
Bahasa Harmony: A Comprehensive Dataset for Bahasa Text-to-Speech Synthesis with Discrete Codec Modeling of EnGen-TTS
Onkar Kishor Susladkar, Vishesh Tripathi, Biddwan Ahmed
This research introduces a comprehensive Bahasa text-to-speech (TTS) dataset and a novel TTS model, EnGen-TTS, designed to enhance the quality and versatility of synthetic speech i…