arXiv (All)
open access
A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books
Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pipeline that uses large language models to extract grammatical rules, example sentences, and lexicons from grammar books and generate synthetic paral
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
- princeton-nlp/SWE-bench#786982
- Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation#627717
- Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models#677553
- Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models#674354
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.#170653
Record · ID 998309
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