29 citations · 100 across the 18 of their papers we have counts for
18 papers
PAL-UI: Planning with Active Look-back for Vision-Based GUI Agents
Zikang Liu, Junyi Li, Wayne Xin Zhao +3
Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) promise human-like interaction with software applications, yet long-horizon tasks remain c…
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie +20
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address…
Trinity-RFT: A General-Purpose and Unified Framework for Reinforcement Fine-Tuning of Large Language Models
Xuchen Pan, Yanxi Chen, Yushuo Chen +11
Trinity-RFT is a general-purpose, unified and easy-to-use framework designed for reinforcement fine-tuning (RFT) of large language models. It is built with a modular and decoupled…
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
Zikang Liu, Kun Zhou, Wayne Xin Zhao +3
Visual instruction tuning has become the predominant technology in eliciting the multimodal task-solving capabilities of large vision-language models (LVLMs). Despite the success,…
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Zitao Li, Fei Wei, Yuexiang Xie +6
Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects…
GenSim: A General Social Simulation Platform with Large Language Model based Agents
Jiakai Tang, Heyang Gao, Xuchen Pan +11
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. Whi…
Very Large-Scale Multi-Agent Simulation in AgentScope
Xuchen Pan, Dawei Gao, Yuexiang Xie +6
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations. However, there remain several challenges w…
AgentScope: A Flexible yet Robust Multi-Agent Platform
Dawei Gao, Zitao Li, Xuchen Pan +16
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooper…
Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
Yingqian Min, Kun Zhou, Dawei Gao +3
Recently, multi-task instruction tuning has been applied into sentence representation learning, which endows the capability of generating specific representations with the guidance…
Data-Juicer: A One-Stop Data Processing System for Large Language Models
Daoyuan Chen, Yilun Huang, Zhijian Ma +10
The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from diffe…
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
Weirui Kuang, Bingchen Qian, Zitao Li +7
LLMs have demonstrated great capabilities in various NLP tasks. Different entities can further improve the performance of those LLMs on their specific downstream tasks by fine-tuni…
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Dawei Gao, Haibin Wang, Yaliang Li +4
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective,…
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Peiyu Liu, Zikang Liu, Ze-Feng Gao +5
Despite the superior performance, Large Language Models~(LLMs) require significant computational resources for deployment and use. To overcome this issue, quantization methods have…
Efficient Personalized Federated Learning via Sparse Model-Adaptation
Daoyuan Chen, Liuyi Yao, Dawei Gao +2
Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. Due to the heterogeneity of clients' local data distribut…
FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie +5
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 bot…
A Benchmark for Federated Hetero-Task Learning
Liuyi Yao, Dawei Gao, Zhen Wang +7
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…
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Daoyuan Chen, Dawei Gao, Weirui Kuang +2
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 statisti…
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Yuexiang Xie, Zhen Wang, Dawei Gao +6
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challen…