266 citations · 739 across the 24 of their papers we have counts for
7 papers · 1 filter
Rubric-Calibrated Preferences: Cross-Query Calibration of LLM Judgments via Item Response Theory
Fabian David Schmidt, Donato Crisostomi, Carlos Lassance +1
Rerankers decide which documents users and LLMs see, yet their standard metric, nDCG, relies on human relevance labels that are costly, sparse, noisy, and discretely graded. As rer…
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval
Justus-Jonas Erker, Nils Reimers, Iryna Gurevych
Decomposition-based multi-hop retrieval methods rely on many autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally ex…
DAPR: A Benchmark on Document-Aware Passage Retrieval
Kexin Wang, Nils Reimers, Iryna Gurevych
The work of neural retrieval so far focuses on ranking short texts and is challenged with long documents. There are many cases where the users want to find a relevant passage withi…
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
Tim Baumgärtner, Leonardo F. R. Ribeiro, Nils Reimers +1
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets. This pipeline covers scenarios like q…
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval
Nandan Thakur, Nils Reimers, Jimmy Lin
Dense retrieval overcome the lexical gap and has shown great success in ad-hoc information retrieval (IR). Despite their success, dense retrievers are expensive to serve across pra…
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
Nandan Thakur, Nils Reimers, Andreas Rücklé +2
Existing neural information retrieval (IR) models have often been studied in homogeneous and narrow settings, which has considerably limited insights into their out-of-distribution…
The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes
Nils Reimers, Iryna Gurevych
Information Retrieval using dense low-dimensional representations recently became popular and showed out-performance to traditional sparse-representations like BM25. However, no pr…