18 citations · 30 across the 12 of their papers we have counts for
13 papers
Reinforcement Fine-Tuning for History-Aware Dense Retriever in RAG
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Retrieval-augmented generation (RAG) enables large language models (LLMs) to produce evidence-based responses, and its performance hinges on the matching between the retriever and…
Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
Guochang Li, Yuchen Liu, Zhen Qin +7
Repository-level software engineering tasks require large language models (LLMs) to efficiently navigate and extract information from complex codebases through multi-turn tool inte…
SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
Yin Wang, Zixuan Wang, Hao Lu +7
Semi-Supervised Learning (SSL) can leverage abundant unlabeled data to boost model performance. However, the class-imbalanced data distribution in real-world scenarios poses great…
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection
Feiyi Chen, Leilei Zhang, Guansong Pang +2
In anomaly detection, methods based on large language models (LLMs) can incorporate expert knowledge by reading professional document, while task-specific small models excel at ext…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…
Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architectures
Yicheng Zhang, Zhen Qin, Zhaomin Wu +2
Large language models (LLMs) are increasingly powering web-based applications, whose effectiveness relies on fine-tuning with large-scale instruction data. However, such data often…
Federated Data-Efficient Instruction Tuning for Large Language Models
Zhen Qin, Zhaomin Wu, Bingsheng He +1
Instruction tuning is a crucial step in improving the responsiveness of pretrained large language models (LLMs) to human instructions. Federated learning (FL) helps to exploit the…
Cluster-Wide Task Slowdown Detection in Cloud System
Feiyi Chen, Yingying Zhang, Lunting Fan +4
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomal…
The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective
Zhen Qin, Daoyuan Chen, Wenhao Zhang +5
The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a br…
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Zhen Qin, Daoyuan Chen, Bingchen Qian +3
Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions. Federated learning offers a way to fine-tune LLMs using…
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Feiyi Chen, Yingying zhang, Zhen Qin +5
Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical…
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection
Feiyi Chen, Zhen Qin, Yingying Zhang +4
Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently.…
Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective
Zhen Qin, Feiyi Chen, Chen Zhi +2
Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The form…