10 citations · 71 across the 49 of their papers we have counts for
5 papers · 1 filter
LTLBench: Towards Benchmarks for Evaluating Temporal Reasoning in Large Language Models
Weizhi Tang, Kwabena Nuamah, Vaishak Belle
Temporal Reasoning (TR) is a critical ability for LLMs to understand and reason over temporal information and relationships between events. To study the TR ability in LLMs, prior w…
Zero, Finite, and Infinite Belief History of Theory of Mind Reasoning in Large Language Models
Weizhi Tang, Vaishak Belle
Large Language Models (LLMs) have recently shown a promise and emergence of Theory of Mind (ToM) ability and even outperform humans in certain ToM tasks. To evaluate and extend the…
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning
Miguel Angel Mendez-Lucero, Enrique Bojorquez Gallardo, Vaishak Belle
Issues of safety, explainability, and efficiency are of increasing concern in learning systems deployed with hard and soft constraints. Symbolic Constrained Learning and Knowledge…
Can Large Language Models put 2 and 2 together? Probing for Entailed Arithmetical Relationships
D. Panas, S. Seth, V. Belle
Two major areas of interest in the era of Large Language Models regard questions of what do LLMs know, and if and how they may be able to reason (or rather, approximately reason).…
ToM-LM: Delegating Theory of Mind Reasoning to External Symbolic Executors in Large Language Models
Weizhi Tang, Vaishak Belle
Theory of Mind (ToM) refers to the ability of individuals to attribute mental states to others. While Large Language Models (LLMs) have shown some promise with ToM ability, they st…