Language Bottleneck

The "language bottleneck" refers to the challenges and opportunities arising from representing complex data (images, actions, or other modalities) using language as an intermediary. Current research focuses on leveraging large language models and iterated learning models to overcome limitations in accuracy and interpretability, particularly within image classification and AI agent design. This approach aims to improve model explainability, generalization, and human-AI interaction by translating non-linguistic data into a linguistic representation, then using this representation for downstream tasks. The resulting advancements have implications for various fields, including computer vision, AI safety, and cross-lingual natural language processing.