2 citations · 5 across the 7 of their papers we have counts for
7 papers
ORBIT: Scalable and Verifiable Data Generation for Search Agents on a Tight Budget
Nandan Thakur, Zijian Chen, Xueguang Ma +1
Search agents, which integrate language models (LMs) with web search, are becoming crucial for answering complex user queries. Constructing training datasets for deep research task…
InterveneBench: Benchmarking LLMs for Intervention Reasoning and Causal Study Design in Real Social Systems
Shaojie Shi, Zhengyu Shi, Lingran Zheng +15
Causal inference in social science relies on end-to-end, intervention-centered research-design reasoning grounded in real-world policy interventions, but current benchmarks fail to…
AgentIR: Reasoning-Aware Retrieval for Deep Research Agents
Zijian Chen, Xueguang Ma, Shengyao Zhuang +3
Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike human users who issue and refine queries without documenting their intermediate t…
BrowseComp-Plus: A More Fair and Transparent Evaluation Benchmark of Deep-Research Agent
Zijian Chen, Xueguang Ma, Shengyao Zhuang +17
Deep-Research agents, which integrate large language models (LLMs) with search tools, have shown success in improving the effectiveness of handling complex queries that require ite…
RankLLM: A Python Package for Reranking with LLMs
Sahel Sharifymoghaddam, Ronak Pradeep, Andre Slavescu +7
The adoption of large language models (LLMs) as rerankers in multi-stage retrieval systems has gained significant traction in academia and industry. These models refine a candidate…
Zero-Shot ATC Coding with Large Language Models for Clinical Assessments
Zijian Chen, John-Michael Gamble, Micaela Jantzi +2
Manual assignment of Anatomical Therapeutic Chemical (ATC) codes to prescription records is a significant bottleneck in healthcare research and operations at Ontario Health and Int…
An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking
Zijian Chen, Ronak Pradeep, Jimmy Lin
Recent advances have demonstrated that large language models (LLMs) excel as listwise rerankers, but their high computational demands remain a barrier to widespread adoption. Furth…