3 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
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…
Repository-level Code Search with Neural Retrieval Methods
Siddharth Gandhi, Luyu Gao, Jamie Callan
This paper presents a multi-stage reranking system for repository-level code search, which leverages the vastly available commit histories of large open-source repositories to aid…
ACER: Automatic Language Model Context Extension via Retrieval
Luyu Gao, Yunyi Zhang, Jamie Callan
Long-context modeling is one of the critical capabilities of language AI for digesting and reasoning over complex information pieces. In practice, long-context capabilities are typ…
SciCode: A Research Coding Benchmark Curated by Scientists
Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang +27
Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evalua…