178 citations · 259 across the 19 of their papers we have counts for
5 papers · 1 filter
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
Xiaobo Xia, Jiale Liu, Shaokun Zhang +3
Coreset selection is powerful in reducing computational costs and accelerating data processing for deep learning algorithms. It strives to identify a small subset from large-scale…
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang +4
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving stron…
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
Qingyun Wu, Gagan Bansal, Jieyu Zhang +11
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are c…
MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
Yiran Wu, Feiran Jia, Shaokun Zhang +7
Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language…
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
Shaokun Zhang, Yiran Wu, Zhonghua Zheng +2
In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test dat…