Byfunctioning overaregion-to-regiongraph,bothmodules can be individualized and adapted to coordinate with visual patterns in each image, guided by specific knowledge forms.

VIENNA performs robust on unseen environments, through learning task-shared, general representations.

Semantic cues and statistical regularities in real-world environment layouts can improve efficiency for navigation in novel environments.

Self-supervised representation learning approaches haverecently surpassed their supervised learning counterparts on downstream tasks like object detection andimage classification.

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from LLMs through iterative feedback and refinement. The main idea is to generate an initial output using an LLMs; then, the same LLMs provides *feedback* for its output and uses it to *refine* itself, iteratively. Self-Refine does not require any supervised training data, additional training, or reinforcement learning, and instead uses a single LLM as the generator, refiner and the feedback provider. We evaluate Self-Refine across 7 diverse tasks, ranging from dialog response generation to mathematical reasoning, using state-of-the-art (GPT-3.5,