61 citations · 252 across the 80 of their papers we have counts for
5 papers · 1 filter
Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?
Yutong Yin, Zhaoran Wang
Humans exhibit remarkable compositional reasoning by integrating knowledge from various sources. For example, if someone learns ( B = f(A) ) from one source and ( C = g(B) ) from a…
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following
Yuxiao Yang, Shenao Zhang, Zhihan Liu +2
This work focuses on building a task planner for Embodied Instruction Following (EIF) using Large Language Models (LLMs). Previous works typically train a planner to imitate expert…
Can Large Language Models Play Games? A Case Study of A Self-Play Approach
Hongyi Guo, Zhihan Liu, Yufeng Zhang +1
Large Language Models (LLMs) harness extensive data from the Internet, storing a broad spectrum of prior knowledge. While LLMs have proven beneficial as decision-making aids, their…
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Yixuan Wang, Ruochen Jiao, Sinong Simon Zhan +5
Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of t…
Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
Zhihan Liu, Hao Hu, Shenao Zhang +4
Large language models (LLMs) demonstrate impressive reasoning abilities, but translating reasoning into actions in the real world remains challenging. In particular, it remains unc…