5 papers
Diversification as Risk Minimization
Rikiya Takehi, Fernando Diaz, Tetsuya Sakai
Users tend to remember failures of a search session more than its many successes. This observation has led to work on search robustness, where systems are penalized if they perform…
Fantastic (small) Retrievers and How to Train Them: mxbai-edge-colbert-v0 Tech Report
Rikiya Takehi, Benjamin Clavié, Sean Lee +1
In this work, we introduce mxbai-edge-colbert-v0 models, at two different parameter counts: 17M and 32M. As part of our research, we conduct numerous experiments to improve retriev…
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…
A General Framework for Off-Policy Learning with Partially-Observed Reward
Rikiya Takehi, Masahiro Asami, Kosuke Kawakami +1
Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only historical interaction data collected under p…
LLM-Assisted Relevance Assessments: When Should We Ask LLMs for Help?
Rikiya Takehi, Ellen M. Voorhees, Tetsuya Sakai +1
Test collections are information-retrieval tools that allow researchers to quickly and easily evaluate ranking algorithms. While test collections have become an integral part of IR…