39 citations · 105 across the 16 of their papers we have counts for
5 papers · 1 filter
AutoIndex: Learning Representation Programs for Retrieval
Sam O'Nuallain, Nithya Rajkumar, Ramya Narayanasamy +3
We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the representations exposed to a retrieval system. Ra…
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…
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents
Nandan Thakur, Jimmy Lin, Sam Havens +3
We introduce FreshStack, a holistic framework for automatically building information retrieval (IR) evaluation benchmarks by incorporating challenging questions and answers. FreshS…
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…
PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai +8
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method,…