6 citations · 24 across the 17 of their papers we have counts for
4 papers · 1 filter
CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action
Lekai Chen, Alvaro Velasquez, Ashutosh Trivedi
Natural-language tasking of embodied agents is rarely just goal specification: users also impose constraints that must persist while the world changes. Code-generating LLM agents c…
Integrating Explanations in Learning LTL Specifications from Demonstrations
Ashutosh Gupta, John Komp, Abhay Singh Rajput +3
This paper investigates whether recent advances in Large Language Models (LLMs) can assist in translating human explanations into a format that can robustly support learning Linear…
Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
Richard Roberson, Gowtham Kaki, Ashutosh Trivedi
This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived. Employing the pop…
Reinforcement Learning with Depreciating Assets
Taylor Dohmen, Ashutosh Trivedi
A basic assumption of traditional reinforcement learning is that the value of a reward does not change once it is received by an agent. The present work forgoes this assumption and…