9 citations · 21 across the 19 of their papers we have counts for
6 papers · 1 filter
Understanding the Embedding Models on Hyper-relational Knowledge Graph
Yubo Wang, Shimin Di, Zhili Wang +4
Recently, Hyper-relational Knowledge Graphs (HKGs) have been proposed as an extension of traditional Knowledge Graphs (KGs) to better represent real-world facts with additional qua…
Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Design
Jialiang Wang, Hanmo Liu, Shimin Di +4
Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail to achieve this balance: neural archit…
Beyond path selection: Better LLMs for Scientific Information Extraction with MimicSFT and Relevance and Rule-induced(R)GRPO
Ran Li, Shimin Di, Yuchen Liu +3
Previous study suggest that powerful Large Language Models (LLMs) trained with Reinforcement Learning with Verifiable Rewards (RLVR) only refines reasoning path without improving t…
Learning Towards Emergence: Paving the Way to Induce Emergence by Inhibiting Monosemantic Neurons on Pre-trained Models
Jiachuan Wang, Shimin Di, Tianhao Tang +4
Emergence, the phenomenon of a rapid performance increase once the model scale reaches a threshold, has achieved widespread attention recently. The literature has observed that mon…
A Selective Learning Method for Temporal Graph Continual Learning
Hanmo Liu, Shimin Di, Haoyang Li +3
Node classification is a key task in temporal graph learning (TGL). Real-life temporal graphs often introduce new node classes over time, but existing TGL methods assume a fixed se…
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation
Xujie Yuan, Yongxu Liu, Shimin Di +6
The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigati…