5 papers · 1 filter
RouterRetriever: Routing over a Mixture of Expert Embedding Models
Hyunji Lee, Luca Soldaini, Arman Cohan +2
Information retrieval methods often rely on a single embedding model trained on large, general-domain datasets like MSMARCO. While this approach can produce a retriever with reason…
mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval
Orion Weller, Benjamin Chang, Eugene Yang +7
Retrieval systems generally focus on web-style queries that are short and underspecified. However, advances in language models have facilitated the nascent rise of retrieval models…
ReFIT: Relevance Feedback from a Reranker during Inference
Revanth Gangi Reddy, Pradeep Dasigi, Md Arafat Sultan +4
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), whi…
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
Orion Weller, Benjamin Chang, Sean MacAvaney +5
Modern Language Models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. While Information Retrieval (IR) models us…
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden +4
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universal…