5 papers
Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level
Ke Chen, Yifeng Wang, Hassan Almosapeeh +1
Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation…
Large Language Model-based Data Science Agent: A Survey
Ke Chen, Peiran Wang, Yaoning Yu +2
The rapid advancement of Large Language Models (LLMs) has driven novel applications across diverse domains, with LLM-based agents emerging as a crucial area of exploration. This su…
IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts
Eric Xue, Ke Chen, Zeyi Huang +2
Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning, but most existing methods share a common limitation: they attem…
Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI
Ke Chen, Haohan Wang
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for…
Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems
Ke Chen, Yufei Zhou, Xitong Zhang +1
Automatic prompt generation plays a crucial role in enabling general-purpose multi-agent systems to perform diverse tasks autonomously. Existing methods typically evaluate prompts…