6 papers
A Compound AI Agent for Conversational Grant Discovery
Zhisheng Tang, Mayank Kejriwal
Research funding discovery remains fundamentally fragmented: researchers navigate disparate agency portals (e.g., in the United States, NSF, NIH, DARPA, Grants.gov, and many others…
Code-Driven Planning in Grid Worlds with Large Language Models
Ashwath Vaithinathan Aravindan, Zhisheng Tang, Mayank Kejriwal
We propose an iterative programmatic planning (IPP) framework for solving grid-based tasks by synthesizing interpretable agent policies expressed in code using large language model…
GRASP: A Grid-Based Benchmark for Evaluating Commonsense Spatial Reasoning
Zhisheng Tang, Mayank Kejriwal
Spatial reasoning, an important faculty of human cognition with many practical applications, is one of the core commonsense skills that is not purely language-based and, for satisf…
Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models
Zhisheng Tang, Mayank Kejriwal
Research on emergent patterns in Large Language Models (LLMs) has gained significant traction in both psychology and artificial intelligence, motivating the need for a comprehensiv…
Is persona enough for personality? Using ChatGPT to reconstruct an agent's latent personality from simple descriptions
Yongyi Ji, Zhisheng Tang, Mayank Kejriwal
Personality, a fundamental aspect of human cognition, contains a range of traits that influence behaviors, thoughts, and emotions. This paper explores the capabilities of large lan…
An Evaluation of Estimative Uncertainty in Large Language Models
Zhisheng Tang, Ke Shen, Mayank Kejriwal
Words of estimative probability (WEPs), such as ''maybe'' or ''probably not'' are ubiquitous in natural language for communicating estimative uncertainty, compared with direct stat…