Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language Models (LLMs). LLMs are improving at handling long context, but still suffer from"lost in the middle"problem. Thus, precise and accurate retrieval is important. Current retrievers chunk lo...
Vineet Kumar, Meghanadh Pulivarthi, Vishwajeet Kumar et al.· 1 citation
SearchWiki paired with WikiResearcher-9B demonstrates that learned navigation over structured corpora is a superior alternative to flat retrieval, and optimizing the agent's navigation policy with on-policy reinforcement learning with a multi-component reward function balancing answer correctness, retrieval quality and...
Guransh Singh, Vishwajeet Kumar, Arkadeep Acharya et al.· 0 citations
It is demonstrated that models trained using ColSNAP maintain near full-resolution retrieval performance under substantial compression and that ColSNAP transfers effectively across multiple late-interaction backbones, and achieves most of its improvements via a lightweight adaptation stage applied to a pre-trained retr...