7 citations · 17 across the 16 of their papers we have counts for
16 papers
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…
Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
Zhichao Xu, Xueguang Ma, Shengyao Zhuang +5
Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure av…
Towards Retrieving Interaction Spaces for Agentic Search
Shengyao Zhuang, Yuansheng Ni, Hengxin Fun +2
Retrieval for search agents is still inherited from non-agentic information retrieval: a retriever ranks the corpus and the agent reads a small set of returned documents. Recent di…
LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum
Zhichao Xu, Shengyao Zhuang, Crystina Zhang +5
While dense retrieval models have been the standard for state-of-the-art information retrieval, their deployment is often constrained by high memory requirements and reliance on GP…
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…
MAGMaR Shared Task System Description: Video Retrieval with OmniEmbed
Jiaqi Samantha Zhan, Crystina Zhang, Shengyao Zhuang +2
Effective video retrieval remains challenging due to the complexity of integrating visual, auditory, and textual modalities. In this paper, we explore unified retrieval methods usi…
Tevatron 2.0: Unified Document Retrieval Toolkit across Scale, Language, and Modality
Xueguang Ma, Luyu Gao, Shengyao Zhuang +3
Recent advancements in large language models (LLMs) have driven interest in billion-scale retrieval models with strong generalization across retrieval tasks and languages. Addition…
Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs
Nandan Thakur, Crystina Zhang, Xueguang Ma +1
Training robust retrieval and reranker models typically relies on large-scale retrieval datasets; for example, the BGE collection contains 1.6 million query-passage pairs sourced f…
Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement Learning
Shengyao Zhuang, Xueguang Ma, Bevan Koopman +2
In this paper, we introduce Rank-R1, a novel LLM-based reranker that performs reasoning over both the user query and candidate documents before performing the ranking task. Existin…
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…
Document Screenshot Retrievers are Vulnerable to Pixel Poisoning Attacks
Shengyao Zhuang, Ekaterina Khramtsova, Xueguang Ma +3
Recent advancements in dense retrieval have introduced vision-language model (VLM)-based retrievers, such as DSE and ColPali, which leverage document screenshots embedded as vector…
VISA: Retrieval Augmented Generation with Visual Source Attribution
Xueguang Ma, Shengyao Zhuang, Bevan Koopman +3
Generation with source attribution is important for enhancing the verifiability of retrieval-augmented generation (RAG) systems. However, existing approaches in RAG primarily link…
Unifying Multimodal Retrieval via Document Screenshot Embedding
Xueguang Ma, Sheng-Chieh Lin, Minghan Li +2
In the real world, documents are organized in different formats and varied modalities. Traditional retrieval pipelines require tailored document parsing techniques and content extr…
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval
Shengyao Zhuang, Xueguang Ma, Bevan Koopman +2
Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: (1) prompt-based re-ranking methods, which require no further training but are onl…
Fine-Tuning LLaMA for Multi-Stage Text Retrieval
Xueguang Ma, Liang Wang, Nan Yang +2
The effectiveness of multi-stage text retrieval has been solidly demonstrated since before the era of pre-trained language models. However, most existing studies utilize models tha…
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…