2 citations · 2 across the 11 of their papers we have counts for
7 papers · 1 filter
Beyond Ranking: Are Decision Models Calibrated Relevance Scorers?
Shengyao Zhuang, Xueguang Ma, Matthias Petri +1
Decision models such as Jev are a new kind of relevance scorer. Their typed score primitive takes a question, an ordered set of textual relevance levels and a state, and returns a…
Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
Zhichao Xu, Xueguang Ma, Shengyao Zhuang +5
Training large dense and mixture-of-experts (MoE) rerankers is constrained by GPU memory and distributed-execution efficiency, which has kept academic reranker research largely on…
Improving Long-Context Retrieval with Multi-Prefix Embedding
Zhenglin Yu, Xueguang Ma, Shengyao Zhuang +4
Long-context retrieval exposes a tension: single-vector embeddings lose fine-grained detail, while token-level multi-vector methods incur prohibitive storage. We propose Multi-Pref…
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…
Layer-wise Token Compression for Efficient Document Reranking
Shengyao Zhuang, Zhichao Xu, Ivano Lauriola
Transformer-based document cross-encoder rerankers are a central component of modern information retrieval systems. Despite their success, these models suffer from high computation…
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…
Leveraging Reference Documents for Zero-Shot Ranking via Large Language Models
Jieran Li, Xiuyuan Hu, Yang Zhao +2
Large Language Models (LLMs) have demonstrated exceptional performance in the task of text ranking for information retrieval. While Pointwise ranking approaches offer computational…