5 papers
Latent Terms: Dense Retrievers Contain Trivially Extractable BM25-ready Zipfian Vocabularies
Benjamin Clavié, Sean Lee, Aamir Shakir +1
We propose Latent Terms, a method revealing that models trained for dense retrieval, whether single- or multi-vector, learn representations that can trivially be decomposed into re…
NumColBERT: Non-Intrusive Numeracy Injection for Late-Interaction Retrieval Models
Haruki Fujimaki, Makoto P. Kato
This study addresses the challenge of improving dense retrieval performance for queries containing numerical conditions, such as ``companies with more than one billion dollars in R…
H-MAPS: Hierarchical Memory-Augmented Proactive Search Assistant for Scientific Literature
Koji Nishikawa, Makoto P. Kato
Scientific reading is an active process that frequently requires consulting external resources, but manual keyword searching interrupts the reading flow and imposes a high cognitiv…
IncompeBench: A Permissively Licensed, Fine-Grained Benchmark for Music Information Retrieval
Benjamin Clavié, Atoof Shakir, Jonah Turner +3
Multimodal Information Retrieval has made significant progress in recent years, leveraging the increasingly strong multimodal abilities of deep pre-trained models to represent info…
Simple Projection Variants Improve ColBERT Performance
Benjamin Clavié, Sean Lee, Rikiya Takehi +2
Multi-vector dense retrieval methods like ColBERT systematically use a single-layer linear projection to reduce the dimensionality of individual vectors. In this study, we explore…