Search Results for author:
Found 21 papers, 11 papers with code
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences.
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Data visualization serves as a critical means for presenting data and mining its valuable insights.
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
Federated Fine-tuning of Large Language Models under Heterogeneous Language Tasks and Client Resources
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing.
Enhancing Multimodal Large Language Models with Vision Detection Models: An Empirical Study
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visual elements.
Ranked #38 on
Visual Question Answering
on MM-Vet
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions.
Data-Juicer: A One-Stop Data Processing System for Large Language Models
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.
Efficient Personalized Federated Learning via Sparse Model-Adaptation
To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.
FS-Real: Towards Real-World Cross-Device Federated Learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.
A Benchmark for Federated Hetero-Task Learning
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.
Learned Index with Dynamic $\epsilon$
We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.
A Pluggable Learned Index Method via Sampling and Gap Insertion
In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Community question answering (CQA) gains increasing popularity in both academy and industry recently.
Cooperative Denoising for Distantly Supervised Relation Extraction
Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.
