From the 1 of 6 linked papers with an AI index.
6 papers
Finding the Right Tables and Columns: A Benchmark and Corpus-Adaptive Embeddings for SQL Schema Retrieval
Qingcheng Zeng, Puxuan Yu, Aman Mehta +2
The paper defines schema retrieval—identifying the relevant tables and columns for a natural‑language question—as a distinct retrieval task and introduces a corpus‑adaptive fine‑tu…
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,…
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…
Dual-View Training for Instruction-Following Information Retrieval
Qingcheng Zeng, Puxuan Yu, Aman Mehta +2
Instruction-following information retrieval (IF-IR) studies retrieval systems that must not only find documents relevant to a query, but also obey explicit user constraints such as…
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…
Arctic-Embed 2.0: Multilingual Retrieval Without Compromise
Puxuan Yu, Luke Merrick, Gaurav Nuti +1
This paper presents the training methodology of Arctic-Embed 2.0, a set of open-source text embedding models built for accurate and efficient multilingual retrieval. While prior wo…