1 citations · 1 across the 6 of their papers we have counts for
6 papers
Progress Reward Model for Reinforcement Learning via Large Language Models
Xiuhui Zhang, Ning Gao, Xingyu Jiang +4
Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large language m…
Unlocking Proactivity in Task-Oriented Dialogue
Azure Zhang, Ning Gao, Yuqin Dai +7
Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance wi…
SAGE: A Service Agent Graph-guided Evaluation Benchmark
Ling Shi, Yuqin Dai, Ziyin Wang +7
The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Existing benchmarks predominan…
RF-Agent: Automated Reward Function Design via Language Agent Tree Search
Ning Gao, Xiuhui Zhang, Xingyu Jiang +3
Designing efficient reward functions for low-level control tasks is a challenging problem. Recent research aims to reduce reliance on expert experience by using Large Language Mode…
Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
Ning Gao, Wei Zhang, Yuqin Dai +6
The rapid evolution of Large Language Models (LLMs) has accelerated the transition from conversational chatbots to general agents. However, effectively balancing empathetic communi…
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
Yuqin Dai, Ning Gao, Wei Zhang +6
Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in service dialogues, as they rely…