14 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…
Rethinking On-policy Optimization for Query Augmentation
Zhichao Xu, Shengyao Zhuang, Xueguang Ma +6
Recent advances in large language models (LLMs) have led to a surge of interest in query augmentation for information retrieval (IR). Two main approaches have emerged. The first pr…
Defragmenting Language Models: An Interpretability-based Approach for Vocabulary Expansion
Maitrey Mehta, Nishant Subramani, Zhichao Xu +2
All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictate the cost and latency of access to contemporary…
InfiniteScienceGym: An Unbounded, Procedurally-Generated Benchmark for Scientific Analysis
Oliver Bentham, Vivek Srikumar
Large language models are emerging as scientific assistants, but evaluating their ability to reason from empirical data remains challenging. Benchmarks derived from published studi…
A Survey of Model Architectures in Information Retrieval
Zhichao Xu, Fengran Mo, Zhiqi Huang +5
The period from 2019 to the present marks one of the most significant paradigm shifts in information retrieval (IR) and natural language processing (NLP), culminating in the emerge…
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…