A large language model is an advanced artificial intelligence system trained on vast amounts of text data, capable of generating human-like responses and understanding natural language queries. It uses deep learning techniques to process and generate coherent and contextually relevant text.
Research explores the effectiveness of using a conversational agent to teach children the socioemotional strategy of "self-talk." Results show that children were able to learn and apply self-talk in their daily lives, offering insights for designing multi-user conversational interfaces.
Researchers propose SayPlan, a scalable approach for large-scale task planning in robotics using large language models (LLMs) grounded in three-dimensional scene graphs (3DSGs). The approach demonstrates high success rates in finding task-relevant subgraphs, reduces input tokens required for representation, and ensures near-perfect executability. While limitations exist, such as graph reasoning constraints and static object assumptions, the study paves the way for improved LLM-based planning in expansive environments.
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