55 citations · 218 across the 20 of their papers we have counts for
20 papers
DLLG: Dynamic Logit-Level Gating of LLM Experts
Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6
Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensemblin…
Do Proactive Agents Need an LLM to Decide When to Act?
Xiaoze Liu, Ruowang Zhang, Amir H. Abdi +5
Proactive assistants continuously decide when to intervene and what context should support the intervention. Large language model (LLM) pipelines repeatedly interpret activity hist…
Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems
Xiaoze Liu, Ruowang Zhang, Weichen Yu +7
Heterogeneous multi-agent systems combine models with different capabilities through a common communication interface. Exchanging internal states directly requires translating betw…
When the Same Coefficients Reach Different Places: Asymmetric Realizability in Transplanting Tokenizers across Large Language Models
Xiaoze Liu, Weichen Yu, Matt Fredrikson +2
Tokenizer transplant in cross-vocabulary model composition reconstructs donor-only embedding rows as weighted combinations over shared lexical anchors and reuses those coefficients…
SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning
Tianyang Xu, Xiaoze Liu, Feijie Wu +2
Large Language Models (LLMs) have transformed natural language processing by learning from massive datasets, yet this rapid progress has also drawn legal scrutiny, as the ability t…
CausalEval: Towards Better Causal Reasoning in Language Models
Longxuan Yu, Delin Chen, Siheng Xiong +6
Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving, decision-making, and understanding the world. While language models (LMs) can generate rat…
Towards Federated RLHF with Aggregated Client Preference for LLMs
Feijie Wu, Xiaoze Liu, Haoyu Wang +3
Reinforcement learning with human feedback (RLHF) fine-tunes a pretrained large language model (LLM) using user preference data, enabling it to generate content aligned with human…
SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation
Xiaoze Liu, Ting Sun, Tianyang Xu +4
Large Language Models (LLMs) have transformed machine learning but raised significant legal concerns due to their potential to produce text that infringes on copyrights, resulting…
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
Tianyang Xu, Shujin Wu, Shizhe Diao +4
Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications. Previous…
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
Xiaoze Liu, Feijie Wu, Tianyang Xu +4
The advent of Large Language Models (LLMs) has significantly transformed the AI landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical concern fo…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
Universal Multi-modal Entity Alignment via Iteratively Fusing Modality Similarity Paths
Bolin Zhu, Xiaoze Liu, Xin Mao +4
The objective of Entity Alignment (EA) is to identify equivalent entity pairs from multiple Knowledge Graphs (KGs) and create a more comprehensive and unified KG. The majority of E…
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
Cunxiang Wang, Xiaoze Liu, Yuanhao Yue +13
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outpu…
MultiEM: Efficient and Effective Unsupervised Multi-Table Entity Matching
Xiaocan Zeng, Pengfei Wang, Yuren Mao +3
Entity Matching (EM), which aims to identify all entity pairs referring to the same real-world entity from relational tables, is one of the most important tasks in real-world data…
Real-time Workload Pattern Analysis for Large-scale Cloud Databases
Jiaqi Wang, Tianyi Li, Anni Wang +7
Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern ana…
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Zeyuan Tan, Xiulong Yuan, Congjie He +7
Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number o…
Unsupervised Entity Alignment for Temporal Knowledge Graphs
Xiaoze Liu, Junyang Wu, Tianyi Li +2
Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend t…
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
Yunjun Gao, Xiaoze Liu, Junyang Wu +3
Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods…
CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration
Congcong Ge, Pengfei Wang, Lu Chen +3
Entity Resolution (ER) aims to identify whether two tuples refer to the same real-world entity and is well-known to be labor-intensive. It is a prerequisite to anomaly detection, a…
LargeEA: Aligning Entities for Large-scale Knowledge Graphs
Congcong Ge, Xiaoze Liu, Lu Chen +2
Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). Current EA approaches suffer from scalability issues, limiting their usage in real-world…