3 citations · 12 across the 10 of their papers we have counts for
8 papers · 1 filter
GENOME: GenerativE Neuro-symbOlic visual reasoning by growing and reusing ModulEs
Zhenfang Chen, Rui Sun, Wenjun Liu +2
Recent works have shown that Large Language Models (LLMs) could empower traditional neuro-symbolic models via programming capabilities to translate language into module description…
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
Junyan Li, Delin Chen, Yining Hong +4
A remarkable ability of human beings resides in compositional reasoning, i.e., the capacity to make "infinite use of finite means". However, current large vision-language foundatio…
Autonomous Tree-search Ability of Large Language Models
Zheyu Zhang, Zhuorui Ye, Yikang Shen +1
Large Language Models have excelled in remarkable reasoning capabilities with advanced prompting techniques, but they fall short on tasks that require exploration, strategic foresi…
Adaptive Online Replanning with Diffusion Models
Siyuan Zhou, Yilun Du, Shun Zhang +5
Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performanc…
Sparse Universal Transformer
Shawn Tan, Yikang Shen, Zhenfang Chen +2
The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers. Empirical evidence shows that UTs have better compositional generalization…
Generalizable Long-Horizon Manipulations with Large Language Models
Haoyu Zhou, Mingyu Ding, Weikun Peng +3
This work introduces a framework harnessing the capabilities of Large Language Models (LLMs) to generate primitive task conditions for generalizable long-horizon manipulations with…
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions
Chengyang Zhao, Yikang Shen, Zhenfang Chen +2
Panoptic Scene Graph has recently been proposed for comprehensive scene understanding. However, previous works adopt a fully-supervised learning manner, requiring large amounts of…
SALMON: Self-Alignment with Instructable Reward Models
Zhiqing Sun, Yikang Shen, Hongxin Zhang +5
Supervised Fine-Tuning (SFT) on response demonstrations combined with Reinforcement Learning from Human Feedback (RLHF) constitutes a powerful paradigm for aligning LLM-based AI ag…