59 citations · 111 across the 8 of their papers we have counts for
8 papers
FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability
Congying Xia, Chen Xing, Jiangshu Du +5
This paper presents FoFo, a pioneering benchmark for evaluating large language models' (LLMs) ability to follow complex, domain-specific formats, a crucial yet underexamined capabi…
UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild
Can Qin, Shu Zhang, Ning Yu +10
Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems. Visual generative foundation models such as Stable Diffus…
HIVE: Harnessing Human Feedback for Instructional Visual Editing
Shu Zhang, Xinyi Yang, Yihao Feng +9
Incorporating human feedback has been shown to be crucial to align text generated by large language models to human preferences. We hypothesize that state-of-the-art instructional…
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation
Jing-Cheng Pang, Xin-Yu Yang, Si-Hang Yang +1
Natural Language-conditioned reinforcement learning (RL) enables the agents to follow human instructions. Previous approaches generally implemented language-conditioned RL by provi…
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao +8
We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MA…
Converse: A Tree-Based Modular Task-Oriented Dialogue System
Tian Xie, Xinyi Yang, Angela S. Lin +13
Creating a system that can have meaningful conversations with humans to help accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI). It has defined the meani…
Learning Efficient Multi-Agent Cooperative Visual Exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao +3
We tackle the problem of cooperative visual exploration where multiple agents need to jointly explore unseen regions as fast as possible based on visual signals. Classical planning…
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin +6
We present GraPPa, an effective pre-training approach for table semantic parsing that learns a compositional inductive bias in the joint representations of textual and tabular data…