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09/15/2023
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Using large language models (LMs) for query or document expansion can im...
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05/22/2023
"According to ..." Prompting Language Models Improves Quoting from Pre-Training Data
Large Language Models (LLMs) may hallucinate and generate fake informati...
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05/12/2023
NevIR: Negation in Neural Information Retrieval
Negation is a common everyday phenomena and has been a consistent area o...
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04/29/2023
Synthetic Cross-language Information Retrieval Training Data
A key stumbling block for neural cross-language information retrieval (C...
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04/24/2023
Overview of the TREC 2022 NeuCLIR Track
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which ai...
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12/20/2022
Parameter-efficient Zero-shot Transfer for Cross-Language Dense Retrieval with Adapters
A popular approach to creating a zero-shot cross-language retrieval mode...
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12/20/2022
When Do Decompositions Help for Machine Reading?
Answering complex questions often requires multi-step reasoning in order...
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12/20/2022
Defending Against Poisoning Attacks in Open-Domain Question Answering
Recent work in open-domain question answering (ODQA) has shown that adve...
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09/03/2022
Multilingual ColBERT-X
ColBERT-X is a dense retrieval model for Cross Language Information Retr...
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06/06/2022
Pretrained Models for Multilingual Federated Learning
Since the advent of Federated Learning (FL), research has applied these ...
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01/24/2022
Patapasco: A Python Framework for Cross-Language Information Retrieval Experiments
While there are high-quality software frameworks for information retriev...
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01/24/2022
HC4: A New Suite of Test Collections for Ad Hoc CLIR
HC4 is a new suite of test collections for ad hoc Cross-Language Informa...
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01/20/2022
Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models
The advent of transformer-based models such as BERT has led to the rise ...
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03/06/2020
