Publications (12)
Beyond Monolingual Deep Research: Evaluating Agents and Retrievers with Cross-Lingual BrowseComp-Plus
Yuheng Lu, Qingcheng Zeng, Heli Qi +6
Deep research agents are increasingly evaluated on their ability to search for evidence, reason over retrieved sources, and produce grounded answers. Existing browsing benchmarks,…
Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers
Qingcheng Zeng, Yuheng Lu, Zeqi Zhou +6
Code-switching is a pervasive linguistic phenomenon in global communication, yet modern information retrieval systems remain predominantly designed for, and evaluated within, monol…
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
Puxuan Yu, Daniel Cohen, Hemank Lamba +2
In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthine…
A Study of Neural Matching Models for Cross-lingual IR
Puxuan Yu, James Allan
In this study, we investigate interaction-based neural matching models for ad-hoc cross-lingual information retrieval (CLIR) using cross-lingual word embeddings (CLWEs). With exper…
Larch: Learned Query Optimization for Semantic Predicates
Fuheng Zhao, Pawel Liskowski, Zihan Li +5
With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, vide…
Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation
Zhiqi Huang, Puxuan Yu, James Allan
Benefiting from transformer-based pre-trained language models, neural ranking models have made significant progress. More recently, the advent of multilingual pre-trained language…