arXiv (All)
open access
SAGA: Score-Weighted Adaptive Generation Alignment for Low-Resource Nordic Language Models
Language models for low-resource languages still make frequent grammatical errors. Alignment methods such as RLHF and DPO can correct them. However, they require human judgements, which are scarce for languages such as Icelandic. Automatic rewards offer an alternative, yet models can learn to exploi
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
- Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models#1130346
- Rethinking Personalized Generation: Test-Time Alignment via Factorized Ranking Models#1108754
- Do Factuality Methods Make LLMs More Factual? Reproducibility and Fair Comparison#1343926
- Evaluating LLM-Generated Preference Distributions#1155935
- XQDT: eXplainable and Quantitative Data-Text Alignment Metric with Feedback Signals#820683
Record · ID 1342577
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