Research map / Large language models / Language, safety and interpretability
Deep networks learn to parse uniform-depth context-free languages from local statistics
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Towards a theory of how the structure of language is acquired by deep neural networks (NeurIPS 2024 · similarity 0.90)
- Short-Context Dominance: How Much Local Context Natural Language Actually Needs? (NeurIPS 2026 · similarity 0.88)
- Improving Unsupervised Constituency Parsing via Maximizing Semantic Information (ICLR 2025 · similarity 0.88)
- In-Context Language Learning: Architectures and Algorithms (ICML 2024 · similarity 0.87)
- The Representation Landscape of Few-Shot Learning and Fine-Tuning in Large Language Models (NeurIPS 2024 · similarity 0.87)
- How Compositional Generalization and Creativity Improve as Diffusion Models are Trained (ICML 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
