44 citations · 49 across the 4 of their papers we have counts for
4 papers
AIAP: A No-Code Workflow Builder for Non-Experts with Natural Language and Multi-Agent Collaboration
Hyunjn An, Yongwon Kim, Wonduk Seo +5
While many tools are available for designing AI, non-experts still face challenges in clearly expressing their intent and managing system complexity. We introduce AIAP, a no-code p…
Emulating Retrieval Augmented Generation via Prompt Engineering for Enhanced Long Context Comprehension in LLMs
Joon Park, Kyohei Atarashi, Koh Takeuchi +1
This paper addresses the challenge of comprehending very long contexts in Large Language Models (LLMs) by proposing a method that emulates Retrieval Augmented Generation (RAG) thro…
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst +8
Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes. Such models are typically outcome-specific, however, requ…
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
William Chen, Siyi Hu, Rajat Talak +1
Abstract semantic 3D scene understanding is a problem of critical importance in robotics. As robots still lack the common-sense knowledge about household objects and locations of a…