50 citations · 73 across the 7 of their papers we have counts for
7 papers
Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams
Yinsicheng Jiang, Liang Cheng, Yeqi Huang +6
LLM agents are increasingly deployed as multi-role teams, where tasks are divided across specialized roles such as planner, executor, and verifier. In these systems, cost and accur…
Learning User Interests via Reasoning and Distillation for Cross-Domain News Recommendation
Mengdan Zhu, Yufan Zhao, Tao Di +2
News recommendation plays a critical role in online news platforms by helping users discover relevant content. Cross-domain news recommendation further requires inferring user's un…
ReCode: Unify Plan and Action for Universal Granularity Control
Zhaoyang Yu, Jiayi Zhang, Huixue Su +9
Real-world tasks require decisions at varying granularities, and humans excel at this by leveraging a unified cognitive representation where planning is fundamentally understood as…
MASTER: A Multi-Agent System with LLM Specialized MCTS
Bingzheng Gan, Yufan Zhao, Tianyi Zhang +5
Large Language Models (LLM) are increasingly being explored for problem-solving tasks. However, their strategic planning capability is often viewed with skepticism. Recent studies…
Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?
Yufan Zhao, Wei Wu, Can Xu
We study knowledge-grounded dialogue generation with pre-trained language models. Instead of pursuing new state-of-the-art on benchmarks, we try to understand if the knowledge stor…
Zero-Resource Knowledge-Grounded Dialogue Generation
Linxiao Li, Can Xu, Wei Wu +3
While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often r…
Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
Yufan Zhao, Can Xu, Wei Wu +1
We study multi-turn response generation for open-domain dialogues. The existing state-of-the-art addresses the problem with deep neural architectures. While these models improved r…