6 citations · 7 across the 4 of their papers we have counts for
5 papers
Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines
Negar Arabzadeh, Andrew Drozdov, Michael Bendersky +1
Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling the generation of multiple se…
Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia +3
Extending a fully post-trained language model with new domain capabilities is fundamentally limited by monolithic training paradigms: retraining from scratch is expensive and scale…
A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, Ashutosh Baheti, Jonathan Chang +13
Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise…
Long Context RAG Performance of Large Language Models
Quinn Leng, Jacob Portes, Sam Havens +2
Retrieval Augmented Generation (RAG) has emerged as a crucial technique for enhancing the accuracy of Large Language Models (LLMs) by incorporating external information. With the a…
Drowning in Documents: Consequences of Scaling Reranker Inference
Mathew Jacob, Erik Lindgren, Matei Zaharia +3
Rerankers, typically cross-encoders, are computationally intensive but are frequently used because they are widely assumed to outperform cheaper initial IR systems. We challenge th…