21 citations · 28 across the 22 of their papers we have counts for
6 papers · 1 filter
Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph
Jingwei Wang, Zai Zhang, Hao Qian +7
Teaching large language models (LLMs) to use tools is crucial for improving their problem-solving abilities and expanding their applications. However, effectively using tools is ch…
Personalized Binomial DAGs Learning with Network Structured Covariates
Boxin Zhao, Weishi Wang, Dingyuan Zhu +5
The causal dependence in data is often characterized by Directed Acyclic Graphical (DAG) models, widely used in many areas. Causal discovery aims to recover the DAG structure using…
Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts
Chunjing Gan, Dan Yang, Binbin Hu +9
In recent years, large language models (LLMs) have made remarkable achievements in various domains. However, the untimeliness and cost of knowledge updates coupled with hallucinati…
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Chunjing Gan, Binbin Hu, Bo Huang +5
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are…
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning
Rong Han, Wenbing Huang, Lingxiao Luo +5
Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for str…
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
Yakun Wang, Binbin Hu, Shuo Yang +6
The rapid development of graph neural networks (GNNs) encourages the rising of link prediction, achieving promising performance with various applications. Unfortunately, through a…