LLMs adapted for Swedish journalism using continued pre-training

Researchers have adapted large language models (LLMs) for Swedish journalism through continued pre-training on a curated dataset of millions of news articles. This adaptation process showed improvements in generation quality and factual knowledge, particularly when combined with experience replay to prevent forgetting. The study also explored parameter-efficient fine-tuning methods like Low Rank Adaptation and found that while generation improved, discriminative tasks did not see similar gains. Crucially, the research highlights the need for domain-specific evaluation benchmarks, as existing Swedish benchmarks failed to accurately reflect the models' in-domain performance. AI

IMPACT Demonstrates methods for improving LLM performance in specialized domains like journalism, potentially enabling more nuanced and context-aware AI applications.

RANK_REASON Academic paper detailing a new method for adapting LLMs to a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Experience Replay Using Transition Sequences.
• Hugging Face
• large language models
• Low Rank Adaptation
• Swedish journalism

• paper
• model release

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