43 citations · 96 across the 32 of their papers we have counts for
9 papers · 1 filter
Provable Scaling Laws for the Test-Time Compute of Large Language Models
Yanxi Chen, Xuchen Pan, Yaliang Li +2
We propose two simple, principled and practical algorithms that enjoy provable scaling laws for the test-time compute of large language models (LLMs). The first one is a two-stage…
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…
Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights
Yanxi Chen, Yaliang Li, Bolin Ding +1
This work presents an analytical framework for the design and analysis of LLM-based algorithms, i.e., algorithms that contain one or multiple calls of large language models (LLMs)…
Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development
Daoyuan Chen, Haibin Wang, Yilun Huang +4
The emergence of multimodal large models has advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models r…
PRICE: A Pretrained Model for Cross-Database Cardinality Estimation
Tianjing Zeng, Junwei Lan, Jiahong Ma +7
Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high…
UniDM: A Unified Framework for Data Manipulation with Large Language Models
Yichen Qian, Yongyi He, Rong Zhu +8
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human…
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…
EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
Xuchen Pan, Yanxi Chen, Yaliang Li +2
This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-paramet…