5 citations · 20 across the 12 of their papers we have counts for
13 papers
Two-Stage Regularization-Based Structured Pruning for LLMs
Mingkuan Feng, Jinyang Wu, Siyuan Liu +7
The deployment of large language models (LLMs) is largely hindered by their large number of parameters. Structural pruning has emerged as a promising solution. Prior structured pru…
AStar: Boosting Multimodal Reasoning with Automated Structured Thinking
Jinyang Wu, Mingkuan Feng, Guocheng Zhai +7
Multimodal large language models excel across diverse domains but struggle with complex visual reasoning tasks. To enhance their reasoning capabilities, current approaches typicall…
DReSS: Data-driven Regularized Structured Streamlining for Large Language Models
Mingkuan Feng, Jinyang Wu, Shuai Zhang +5
Large language models (LLMs) have achieved significant progress across various domains, but their increasing scale results in high computational and memory costs. Recent studies ha…
Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTS
Jinyang Wu, Mingkuan Feng, Shuai Zhang +4
In-context learning (ICL) enables large language models (LLMs) to perform downstream tasks through advanced prompting and high-quality demonstrations. However, traditional ICL para…
Pandora's Box or Aladdin's Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models
Jinyang Wu, Shuai Zhang, Feihu Che +4
Retrieval-Augmented Generation (RAG) has emerged as a crucial method for addressing hallucinations in large language models (LLMs). While recent research has extended RAG models to…
Can large language models understand uncommon meanings of common words?
Jinyang Wu, Feihu Che, Xinxin Zheng +5
Large language models (LLMs) like ChatGPT have shown significant advancements across diverse natural language understanding (NLU) tasks, including intelligent dialogue and autonomo…
KS-LLM: Knowledge Selection of Large Language Models with Evidence Document for Question Answering
Xinxin Zheng, Feihu Che, Jinyang Wu +4
Large language models (LLMs) suffer from the hallucination problem and face significant challenges when applied to knowledge-intensive tasks. A promising approach is to leverage ev…
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
Pengpeng Shao, Tong Liu, Feihu Che +2
Temporal knowledge prediction is a crucial task for the event early warning that has gained increasing attention in recent years, which aims to predict the future facts by using re…
MixKG: Mixing for harder negative samples in knowledge graph
Feihu Che, Guohua Yang, Pengpeng Shao +2
Knowledge graph embedding~(KGE) aims to represent entities and relations into low-dimensional vectors for many real-world applications. The representations of entities and relation…
Knowledge graph enhanced recommender system
Zepeng Huai, Jianhua Tao, Feihu Che +2
Knowledge Graphs (KGs) have shown great success in recommendation. This is attributed to the rich attribute information contained in KG to improve item and user representations as…
Multi-Level Graph Contrastive Learning
Pengpeng Shao, Tong Liu, Dawei Zhang +3
Graph representation learning has attracted a surge of interest recently, whose target at learning discriminant embedding for each node in the graph. Most of these representation m…
Tucker decomposition-based Temporal Knowledge Graph Completion
Pengpeng Shao, Guohua Yang, Dawei Zhang +3
Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the…
Self-supervised Graph Representation Learning via Bootstrapping
Feihu Che, Guohua Yang, Dawei Zhang +3
Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNN…