28 citations · 71 across the 24 of their papers we have counts for
8 papers · 1 filter
RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation
Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru +4
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving external data to mitigate hallucinations and outdated knowledge issues. Benefiting from the…
Towards Unified Neurosymbolic Reasoning on Knowledge Graphs
Qika Lin, Fangzhi Xu, Hao Lu +5
Knowledge Graph (KG) reasoning has received significant attention in the fields of artificial intelligence and knowledge engineering, owing to its ability to autonomously deduce ne…
Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Haoran Luo, Haihong E, Guanting Chen +8
Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. Graph…
MUR: Momentum Uncertainty guided Reasoning for Large Language Models
Hang Yan, Fangzhi Xu, Rongman Xu +8
Large Language Models have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scalin…
-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation
Fangzhi Xu, Hang Yan, Chang Ma +4
Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness…
GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction
Jian Zhang, Bifan Wei, Shihao Qi +3
The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language…
MAPS: Multi-Agent Personality Shaping for Collaborative Reasoning
Jian Zhang, Zhiyuan Wang, Zhangqi Wang +6
Collaborative reasoning with multiple agents offers the potential for more robust and diverse problem-solving. However, existing approaches often suffer from homogeneous agent beha…
MARS: Multi-Agent Adaptive Reasoning with Socratic Guidance for Automated Prompt Optimization
Jian Zhang, Zhangqi Wang, Haiping Zhu +6
Large language models (LLMs) typically operate in a question-answering paradigm, where the quality of the input prompt critically affects the response. Automated Prompt Optimizatio…