Language Models Represent and Transform Concepts with Shared Geometry
📰 ArXiv cs.AI
Discover how language models represent concepts with shared geometry and transform them with contextual vector fields, a breakthrough in understanding neural networks
Action Steps
- Apply neural population geometry to analyze concept representations in language models
- Use point-cloud manifolds to formalize concept representations
- Instantiate vector fields to model contextual transformations
- Analyze the shared geometry of concept representations in large language models
- Implement this framework to improve language model performance on contextual tasks
Who Needs to Know This
NLP researchers and AI engineers can apply this knowledge to improve language model performance and develop more effective contextual understanding, benefiting teams working on language-based AI projects
Key Insight
💡 Concept representations in language models are not stationary, but are transformed by context, and can be formalized using neural population geometry
Share This
💡 Language models represent concepts with shared geometry & transform them with contextual vector fields! #LLMs #NLP
Key Takeaways
Discover how language models represent concepts with shared geometry and transform them with contextual vector fields, a breakthrough in understanding neural networks
Full Article
Title: Language Models Represent and Transform Concepts with Shared Geometry
Abstract:
arXiv:2607.04525v1 Announce Type: cross Abstract: How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as stationary geometric objects. Yet concepts appear in context, and context transforms them. Drawing from neural population geometry, we formalize concept representations as point-cloud manifolds and contextual transformations as vector fields, and instantiate this framework in large language models. Acr
Abstract:
arXiv:2607.04525v1 Announce Type: cross Abstract: How concepts are represented in neural networks is a fundamental question in machine learning. The dominant view treats concept representations as stationary geometric objects. Yet concepts appear in context, and context transforms them. Drawing from neural population geometry, we formalize concept representations as point-cloud manifolds and contextual transformations as vector fields, and instantiate this framework in large language models. Acr
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