5 citations · 7 across the 13 of their papers we have counts for
5 papers · 1 filter
Evaluating Creativity and Deception in Large Language Models: A Simulation Framework for Multi-Agent Balderdash
Parsa Hejabi, Elnaz Rahmati, Alireza S. Ziabari +3
Large Language Models (LLMs) have shown impressive capabilities in complex tasks and interactive environments, yet their creativity remains underexplored. This paper introduces a s…
When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models
Ting-Yun Chang, Jesse Thomason, Robin Jia
This paper studies in-context learning by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components). We observe cur…
Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback
Wang Zhu, Ishika Singh, Robin Jia +1
Symbolic planners can discover a sequence of actions from initial to goal states given expert-defined, domain-specific logical action semantics. Large Language Models (LLMs) can di…
TwoStep: Multi-agent Task Planning using Classical Planners and Large Language Models
David Bai, Ishika Singh, David Traum +1
Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are poss…
THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
Wilbert Pumacay, Ishika Singh, Jiafei Duan +3
To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a major…