2 citations · 4 across the 28 of their papers we have counts for
31 papers
CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants
Xuzhao Geng, Haozhao Wang, Xuelian Li +4
Target-oriented dialogue systems have demonstrated strong capabilities in completing user goals through interactive conversations. However, existing studies are primarily designed…
Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models
Shilin Yan, Jintao Tong, Hongwei Xue +6
The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit…
Administrative Decentralization in Edge-Cloud Multi-Agent for Mobile Automation
Senyao Li, Zhigang Zuo, Haozhao Wang +3
Collaborative edge-cloud frameworks have emerged as the main- stream paradigm for mobile automation, mitigating the latency and privacy risks inherent to monolithic cloud agents. H…
Unbiased Rectification for Sequential Recommender Systems Under Fake Orders
Qiyu Qin, Yichen Li, Haozhao Wang +3
Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactions, including click farming, c…
Nonlinearity as Rank: Generative Low-Rank Adapter with Radial Basis Functions
Yihao Ouyang, Shiwei Li, Haozhao Wang +7
Low-rank adaptation (LoRA) approximates the update of a pretrained weight matrix using the product of two low-rank matrices. However, standard LoRA follows an explicit-rank paradig…
Are We Evaluating the Edit Locality of LLM Model Editing Properly?
Wei Liu, Haomei Xu, Hongkai Liu +5
Model editing has recently emerged as a popular paradigm for efficiently updating knowledge in LLMs. A central desideratum of updating knowledge is to balance editing efficacy, i.e…
FAIR: Focused Attention Is All You Need for Generative Recommendation
Longtao Xiao, Haolin Zhang, Guohao Cai +6
Recently, transformer-based generative recommendation has garnered significant attention for user behavior modeling. However, it often requires discretizing items into multi-code r…
Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation
Wei Liu, Haomei Xu, Bingqing Liu +6
Large language models (LLMs) inevitably encode outdated or incorrect knowledge. Updating, deleting, and forgetting such knowledge is important for alignment, safety, and other issu…
FedBiF: Communication-Efficient Federated Learning via Bits Freezing
Shiwei Li, Qunwei Li, Haozhao Wang +3
Federated learning (FL) is an emerging distributed machine learning paradigm that enables collaborative model training without sharing local data. Despite its advantages, FL suffer…
BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity
Shiwei Li, Xiandi Luo, Haozhao Wang +6
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). It approximates the update of a pretrained weight matrix…
Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Senyao Li, Haozhao Wang, Wenchao Xu +6
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost,…
Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
Yifan Wang, Weinan Gan, Longtao Xiao +7
Generative recommendation (GR) typically encodes behavioral or semantic aspects of item information into discrete tokens, leveraging the standard autoregressive (AR) generation par…
FedRE: Robust and Effective Federated Learning with Privacy Preference
Tianzhe Xiao, Yichen Li, Yu Zhou +6
Despite Federated Learning (FL) employing gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data, private information can still be d…
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
Wei Liu, Zhongyu Niu, Lang Gao +4
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, an…
A Systematic Survey on Federated Sequential Recommendation
Yichen Li, Qiyu Qin, Gaoyang Zhu +5
Sequential recommendation is an advanced recommendation technique that utilizes the sequence of user behaviors to generate personalized suggestions by modeling the temporal depende…
FastCache: Optimizing Multimodal LLM Serving through Lightweight KV-Cache Compression Framework
Jianian Zhu, Hang Wu, Haojie Wang +4
Multi-modal Large Language Models (MLLMs) serving systems commonly employ KV-cache compression to reduce memory footprint. However, existing compression methods introduce significa…
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
Wei Liu, Zhiying Deng, Zhongyu Niu +4
Extracting a small subset of crucial rationales from the full input is a key problem in explainability research. The most widely used fundamental criterion for rationale extraction…
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables
Xuzhao Geng, Haozhao Wang, Jun Wang +2
Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles…
UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration
Longtao Xiao, Haozhao Wang, Cheng Wang +6
With the rise of generative paradigms, generative recommendation has garnered increasing attention. The core component is the item code, generally derived by quantizing collaborati…
Resource-Constrained Federated Continual Learning: What Does Matter?
Yichen Li, Yuying Wang, Jiahua Dong +4
Federated Continual Learning (FCL) aims to enable sequentially privacy-preserving model training on streams of incoming data that vary in edge devices by preserving previous knowle…
FedGIG: Graph Inversion from Gradient in Federated Learning
Tianzhe Xiao, Yichen Li, Yining Qi +2
Recent studies have shown that Federated learning (FL) is vulnerable to Gradient Inversion Attacks (GIA), which can recover private training data from shared gradients. However, ex…
Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey
Yichen Li, Haozhao Wang, Wenchao Xu +9
Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-st…
Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence
Yichen Li, Yuying Wang, Haozhao Wang +3
Continual Federated Learning (CFL) allows distributed devices to collaboratively learn novel concepts from continuously shifting training data while avoiding knowledge forgetting o…
Is the MMI Criterion Necessary for Interpretability? Degenerating Non-causal Features to Plain Noise for Self-Rationalization
Wei Liu, Zhiying Deng, Zhongyu Niu +4
An important line of research in the field of explainability is to extract a small subset of crucial rationales from the full input. The most widely used criterion for rationale ex…