arXiv (All)
open access
A Scaled-Up Empirical Study of Syntactic Language Models
Syntactic language models (SLMs) that generate sentences along with their syntactic tree structures have shown promise in both syntactic generalization and downstream tasks. Prior studies, however, have mostly evaluated smaller models trained on less pretraining data and tested on narrower task suit
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
- Official Review · ICLR 2025#787046
- Enhancing Automated Unit Test Generation for NLP Libraries Using Large Language Models#1084706
- Enhancing Automated Unit Test Generation for NLP Libraries Using Large Language Models#919499
- Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks#394448
- A systematic framework for generating novel experimental hypotheses from language models#128303
Record · ID 1341720
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