4 papers
CAPO: Constraint-Aware Prompt Optimization for LLM Agents
Victor Ye Dong, Reid Pryzant, Yi Liu +1
Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks. Such deployments impose distinct operational requireme…
Aligning the Query Space: Greedy Information Projection for Language Model Data Selection
Victor Ye Dong, Kuan-Yun Lee, Jiamei Shuai +3
Data selection for language models is often framed as balancing example quality and diversity. We argue that both are consequences of a more fundamental principle: selected example…
DeepThink: Aligning Language Models with Domain-Specific User Intents
Yang Li, Mingxuan Luo, Yeyun Gong +4
Supervised fine-tuning with synthesized instructions has been a common practice for adapting LLMs to domain-specific QA tasks. However, the synthesized instructions deviate from re…
Task Oriented In-Domain Data Augmentation
Xiao Liang, Xinyu Hu, Simiao Zuo +5
Large Language Models (LLMs) have shown superior performance in various applications and fields. To achieve better performance on specialized domains such as law and advertisement,…