How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study

Discover that LLMs and VLMs can understand complex spatial concepts like viewpoint rotation using only linguistic input. Leverage this inherent linguistic spatial intelligence to design more effective prompts for text-only reasoning tasks and build robust AI systems.

beginner30 min5 steps

The play

Acknowledge Linguistic Spatial Reasoning
Understand that LLMs and VLMs possess an inherent capability to reason about spatial transformations and viewpoint rotation solely from textual information, even without visual input.
Formulate Spatial Reasoning Prompts
Craft specific prompts designed to test and utilize the model's spatial understanding. Focus on scenarios involving relative positions, object orientations, and transformations described purely in text.
Analyze Model Responses for Spatial Accuracy
Evaluate the LLM/VLM's responses to your spatial prompts. Look for accurate descriptions of new positions, orientations, or relationships that demonstrate an understanding of the described spatial changes.
Refine Prompts for Enhanced Spatial Inference
Based on your analysis, iterate on your prompt engineering. Adjust wording, add more context, or break down complex spatial problems into smaller steps to elicit more precise and robust spatial reasoning from the model.
Apply to Text-Only Spatial Tasks
Integrate this refined prompting strategy into applications requiring spatial reasoning without visual data, such as robotics control from text commands, virtual environment descriptions, or accessibility tools.

Starter code

Tell me the new position: 'An apple is on a table. A book is to the left of the apple. If you move the table 1 meter to the right, where is the book relative to the apple?'

Source

Paperarxiv.org