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cs.CL2025★ 1 cited
Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey
Shengyue Guan, Jindong Wang, Jiang Bian +3
This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings. Using a PRISMA-inspired framework, we systematically revi…
cs.CL2024
AMPO: Automatic Multi-Branched Prompt Optimization
Sheng Yang, Yurong Wu, Yan Gao +10
Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple pattern…
cs.CL2024
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Yurong Wu, Yan Gao, Bin Benjamin Zhu +6
Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt…