53 citations · 156 across the 6 of their papers we have counts for
6 papers
GPT-4 Doesn't Know It's Wrong: An Analysis of Iterative Prompting for Reasoning Problems
Kaya Stechly, Matthew Marquez, Subbarao Kambhampati
There has been considerable divergence of opinion on the reasoning abilities of Large Language Models (LLMs). While the initial optimism that reasoning might emerge automatically w…
Can Large Language Models Really Improve by Self-critiquing Their Own Plans?
Karthik Valmeekam, Matthew Marquez, Subbarao Kambhampati
There have been widespread claims about Large Language Models (LLMs) being able to successfully verify or self-critique their candidate solutions in reasoning problems in an iterat…
On the Planning Abilities of Large Language Models : A Critical Investigation
Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan +1
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)
Karthik Valmeekam, Sarath Sreedharan, Matthew Marquez +2
Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to e…
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
Utkarsh Soni, Nupur Thakur, Sarath Sreedharan +4
There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected t…
PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change
Karthik Valmeekam, Matthew Marquez, Alberto Olmo +2
Generating plans of action, and reasoning about change have long been considered a core competence of intelligent agents. It is thus no surprise that evaluating the planning and re…