67 citations · 80 across the 20 of their papers we have counts for
5 papers · 1 filter
PIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation
Yichuan Wang, Zhifei Li, Zirui Wang +5
Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipe…
RAG over Thinking Traces Can Improve Reasoning Tasks
Negar Arabzadeh, Wenjie Ma, Sewon Min +1
Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as ma…
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…
DS SERVE: A Framework for Efficient and Scalable Neural Retrieval
Jinjian Liu, Yichuan Wang, Xinxi Lyu +4
We present DS-Serve, a framework that transforms large-scale text datasets, comprising half a trillion tokens, into a high-performance neural retrieval system. DS-Serve offers both…
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…