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UniCode: Learning a Unified Codebook for Multimodal Large Language Models
In this paper, we propose \textbf{UniCode}, a novel approach within the domain of multimodal large language models (MLLMs) that learns a unified codebook to efficiently tokenize visual, text, and potentially other types of signals.
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds
Steve-Eye integrates the LLM with a visual encoder which enables it to process visual-text inputs and generate multimodal feedback.
LLaMA Rider: Spurring Large Language Models to Explore the Open World
Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world conditions.
Learning Multi-Object Positional Relationships via Emergent Communication
The study of emergent communication has been dedicated to interactive artificial intelligence.
Multi-Agent Language Learning: Symbolic Mapping
We find that symbolic mapping learned in simple referential games can notably promote language learning in difficult tasks.
