7 citations · 9 across the 10 of their papers we have counts for
4 papers · 1 filter
Tevatron-Elastic: A Unified Abstraction for Training Elastic Retrievers and Rerankers
Yu Wang, Shengyao Zhuang, Xueguang Ma +4
A single model scale challenges the flexibility of a production retrieval system: some settings need it faster, others need a smaller index, and the right trade-off changes with th…
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…
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers
Xueguang Ma, Xi Victoria Lin, Barlas Oguz +3
Large language models (LLMs) have demonstrated strong effectiveness and robustness while fine-tuned as dense retrievers. However, their large parameter size brings significant infe…
Found in the Middle: Permutation Self-Consistency Improves Listwise Ranking in Large Language Models
Raphael Tang, Xinyu Zhang, Xueguang Ma +2
Large language models (LLMs) exhibit positional bias in how they use context, which especially complicates listwise ranking. To address this, we propose permutation self-consistenc…