13 citations · 25 across the 26 of their papers we have counts for
28 papers
MPrune: Hierarchical Collaborative Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation
Taolin Zhang, Weizi shao, Zijie Zhou +5
Recent advances in multi-modal retrieval-augmented generation (mRAG), which augments multi-modal large language models (MLLMs) with external knowledge, have shown that collective i…
Learning Transferable Topology Priors for Multi-Agent LLM Collaboration Across Domains
Taolin Zhang, Zijie Zhou, Jiuheng Wan +4
Large language model (LLM)-based multi-agent systems have shown strong potential for complex reasoning by coordinating specialized agents through structured communication. However,…
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
Taolin Zhang, Dongyang Li, Chen Chen +5
Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These diffic…
Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution
Taolin Zhang, Pukun Zhao, Qizhou Chen +5
Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall efficiency, existing methods o…
An Information-Theoretic Framework for Robust Large Language Model Editing
Qizhou Chen, Chengyu Wang, Taolin Zhang +1
Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdate…
MPrune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation
Weizi Shao, Taolin Zhang, Zijie Zhou +3
Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that th…
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs
Taolin Zhang, Haidong Kang, Dongyang Li +3
Recently, large language models (LLMs) have demonstrated impressive results but still suffer from hallucinations. Model editing has been proposed to correct factual inaccuracies in…
BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering
Taolin Zhang, Dongyang Li, Qizhou Chen +2
Multi-hop question answering (QA) involves finding multiple relevant passages and performing step-by-step reasoning to answer complex questions. Previous works on multi-hop QA empl…
UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models
Qizhou Chen, Dakan Wang, Taolin Zhang +4
Model editing aims to enhance the accuracy and reliability of large language models (LLMs) by efficiently adjusting their internal parameters. Currently, most LLM editing datasets…
A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions
Chengyu Wang, Taolin Zhang, Richang Hong +1
Recently, the reasoning capabilities of large reasoning models (LRMs), such as DeepSeek-R1, have seen significant advancements through the slow thinking process. Despite these achi…
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
Qizhou Chen, Chengyu Wang, Dakan Wang +3
Model editing aims to correct inaccurate knowledge, update outdated information, and incorporate new data into Large Language Models (LLMs) without the need for retraining. This ta…
Attribution Analysis Meets Model Editing: Advancing Knowledge Correction in Vision Language Models with VisEdit
Qizhou Chen, Taolin Zhang, Chengyu Wang +3
Model editing aims to correct outdated or erroneous knowledge in large models without costly retraining. Recent research discovered that the mid-layer representation of the subject…
R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models
Taolin Zhang, Dongyang Li, Qizhou Chen +5
Retrieval-augmented large language models (LLMs) leverage relevant content retrieved by information retrieval systems to generate correct responses, aiming to alleviate the halluci…
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning
Dongyang Li, Taolin Zhang, Longtao Huang +3
Knowledge-enhanced pre-trained language models (KEPLMs) leverage relation triples from knowledge graphs (KGs) and integrate these external data sources into language models via sel…
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding
Dongyang Li, Taolin Zhang, Jiali Deng +4
Cross-lingual representation learning transfers knowledge from resource-rich data to resource-scarce ones to improve the semantic understanding abilities of different languages. Ho…
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models
Dongyang Li, Junbing Yan, Taolin Zhang +5
Retrieval augmented generation (RAG) exhibits outstanding performance in promoting the knowledge capabilities of large language models (LLMs) with retrieved documents related to us…
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models
Taolin Zhang, Qizhou Chen, Dongyang Li +5
Recently, while large language models (LLMs) have demonstrated impressive results, they still suffer from hallucination, i.e., the generation of false information. Model editing is…
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning
Qizhou Chen, Taolin Zhang, Xiaofeng He +4
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging…
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +5
KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding. Previous language models facilitated knowledge acquisition by incorporating knowled…
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
Ruyao Xu, Taolin Zhang, Chengyu Wang +6
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the performance of various downstream NLP tasks by injecting knowledge facts from large-scale Knowledge Graphs (KGs)…
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
Junbing Yan, Chengyu Wang, Taolin Zhang +3
Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps. Yet, complex logical reasoning…
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Taolin Zhang, Junwei Dong, Jianing Wang +6
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguisti…
FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs
Taolin Zhang, Chuan Chen, Yaomin Chang +2
As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, in some prac…
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
Chengyu Wang, Minghui Qiu, Chen Shi +7
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP). Yet, it is not easy to obtain high-performing models and deploy them onl…