4 citations · 4 across the 6 of their papers we have counts for
6 papers
Code Models are Zero-shot Precondition Reasoners
Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu +5
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point. This work…
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Dong-Ki Kim, Sungryull Sohn, Lajanugen Logeswaran +2
Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL). This approach offers important advantages, such as generating…
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
Zheyuan Zhang, Shane Storks, Fengyuan Hu +4
Pre-trained language models (PLMs) have shown impressive performance in various language tasks. However, they are prone to spurious correlations, and often generate illusory inform…
A Picture is Worth a Thousand Words: Language Models Plan from Pixels
Anthony Z. Liu, Lajanugen Logeswaran, Sungryull Sohn +1
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models…
Multimodal Subtask Graph Generation from Instructional Videos
Yunseok Jang, Sungryull Sohn, Lajanugen Logeswaran +3
Real-world tasks consist of multiple inter-dependent subtasks (e.g., a dirty pan needs to be washed before it can be used for cooking). In this work, we aim to model the causal dep…
Unsupervised Task Graph Generation from Instructional Video Transcripts
Lajanugen Logeswaran, Sungryull Sohn, Yunseok Jang +2
This work explores the problem of generating task graphs of real-world activities. Different from prior formulations, we consider a setting where text transcripts of instructional…