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Found 5 papers, 2 papers with code
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models
The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability.
Improved Learned Sparse Retrieval with Corpus-Specific Vocabularies
We explore leveraging corpus-specific vocabularies that improve both efficiency and effectiveness of learned sparse retrieval systems.
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval
In this paper, we introduce the approach behind our submission for the MIRACL challenge, a WSDM 2023 Cup competition that centers on ad-hoc retrieval across 18 diverse languages.
Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation
Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models.
A Study of Neural Matching Models for Cross-lingual IR
In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs).
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
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