most citedDynamic Planning with a LLM

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2023

Dialogue-based generation of self-driving simulation scenarios using Large Language Models

Antonio Valerio Miceli-Barone, Alex Lascarides, Craig Innes

Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars. Current simulation frameworks are driven by highly-specialist domain specific lang…

cs.CL20232 cited

Dynamic Planning with a LLM

Gautier Dagan, Frank Keller, Alex Lascarides

While Large Language Models (LLMs) can solve many NLP tasks in zero-shot settings, applications involving embodied agents remain problematic. In particular, complex plans that requ…

cs.CL2023

Interactive Acquisition of Fine-grained Visual Concepts by Exploiting Semantics of Generic Characterizations in Discourse

Jonghyuk Park, Alex Lascarides, Subramanian Ramamoorthy

Interactive Task Learning (ITL) concerns learning about unforeseen domain concepts via natural interactions with human users. The learner faces a number of significant constraints:…

cs.CL2023

Learning Manner of Execution from Partial Corrections

Mattias Appelgren, Alex Lascarides

Some actions must be executed in different ways depending on the context. For example, wiping away marker requires vigorous force while wiping away almonds requires more gentle for…

cs.CL2023

Learning the Effects of Physical Actions in a Multi-modal Environment

Gautier Dagan, Frank Keller, Alex Lascarides

Large Language Models (LLMs) handle physical commonsense information inadequately. As a result of being trained in a disembodied setting, LLMs often fail to predict an action's out…