33 citations · 62 across the 18 of their papers we have counts for
5 papers · 1 filter
Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Yaxi Lu, Shenzhi Yang, Cheng Qian +12
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scena…
Aligning LLMs with Individual Preferences via Interaction
Shujin Wu, May Fung, Cheng Qian +3
As large language models (LLMs) demonstrate increasingly advanced capabilities, aligning their behaviors with human values and preferences becomes crucial for their wide adoption.…
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
Bingxiang He, Ning Ding, Cheng Qian +10
Understanding alignment techniques begins with comprehending zero-shot generalization brought by instruction tuning, but little of the mechanism has been understood. Existing work…
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents
Cheng Qian, Bingxiang He, Zhong Zhuang +8
Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. Although adep…
Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
Cheng Qian, Shihao Liang, Yujia Qin +6
This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution. Unlike…