arXiv (OAI Expanded)
open access
Mimir: Large-scale Multilingual Concept Modeling
Current language modeling approaches are built around tokens. Text corpora are split into tokens, and models are trained by performing computations on these tokens, such as predicting the next token given the preceding ones as context. This paradigm has become the standard in modern language modelin
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Lost in Reordering: Structural Sensitivity of Multilingual LLMs under Semantics-Preserving Perturbations#926425
- Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions#285464
- CENDRe: Concept Extraction with Natural Domain Representations#422235
- A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage#335847
- The Versatility of Large Language Models: A Comprehensive Review and Structured Survey of Architectures, Applications, Challenges, and Future Trajectories#447064
Record · ID 1084219
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive