9 citations · 18 across the 11 of their papers we have counts for
7 papers · 1 filter
SOFA-FL: Self-Organizing Hierarchical Federated Learning with Adaptive Clustered Data Sharing
Yi Ni, Xinkun Wang, Han Zhang
Federated Learning (FL) faces significant challenges in evolving environments, particularly regarding data heterogeneity and the rigidity of fixed network topologies. To address th…
SELF: A Robust Singular Value and Eigenvalue Approach for LLM Fingerprinting
Hanxiu Zhang, Yue Zheng
The protection of Intellectual Property (IP) in Large Language Models (LLMs) represents a critical challenge in contemporary AI research. While fingerprinting techniques have emerg…
RAE: A Neural Network Dimensionality Reduction Method for Nearest Neighbors Preservation in Vector Search
Han Zhang, Dongfang Zhao
While high-dimensional embedding vectors are being increasingly employed in various tasks like Retrieval-Augmented Generation and Recommendation Systems, popular dimensionality red…
ODIA: Oriented Distillation for Inline Acceleration of LLM-based Function Calling
Hanlong Zhang, Jingsheng Yang, Hao Li +2
Function Calling is a crucial technique that enables Large Language Models (LLMs) to interact with external systems through APIs. However, the high latency associated with LLM-base…
Assay2Mol: large language model-based drug design using BioAssay context
Yifan Deng, Spencer S. Ericksen, Anthony Gitter
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…
FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging
Zichen Tang, Haihong E, Ziyan Ma +10
We introduce FinanceReasoning, a novel benchmark designed to evaluate the reasoning capabilities of large reasoning models (LRMs) in financial numerical reasoning problems. Compare…
Protein Large Language Models: A Comprehensive Survey
Yijia Xiao, Wanjia Zhao, Junkai Zhang +12
Protein-specific large language models (Protein LLMs) are revolutionizing protein science by enabling more efficient protein structure prediction, function annotation, and design.…