18 citations · 73 across the 11 of their papers we have counts for
13 papers
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents
Yue Wu, So Yeon Min, Yonatan Bisk +5
Pre-trained large language models (LLMs) capture procedural knowledge about the world. Recent work has leveraged LLM's ability to generate abstract plans to simplify challenging co…
SPRING: Studying the Paper and Reasoning to Play Games
Yue Wu, Shrimai Prabhumoye, So Yeon Min +5
Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement le…
The Internal State of an LLM Knows When It's Lying
Amos Azaria, Tom Mitchell
While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a c…
Zero-shot Triplet Extraction by Template Infilling
Bosung Kim, Hayate Iso, Nikita Bhutani +3
The task of triplet extraction aims to extract pairs of entities and their corresponding relations from unstructured text. Most existing methods train an extraction model on traini…
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules
Forough Arabshahi, Jennifer Lee, Antoine Bosselut +2
One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common se…
Towards General Natural Language Understanding with Probabilistic Worldbuilding
Abulhair Saparov, Tom M. Mitchell
We introduce the Probabilistic Worldbuilding Model (PWM), a new fully-symbolic Bayesian model of semantic parsing and reasoning, as a first step in a research program toward more d…