arxiv
PublishedOctober 5, 2026 at 4:00 AM
Specializing Without Forgetting: Analyzing Knowledge Preservation in Multilingual Model Adaptation
Publisher summary· verbatim
arXiv:2606.00284v2 Announce Type: replace Abstract: While continual pretraining (CPT) is a practical way to extend large language models to new languages, na\"ive finetuning often erodes existing capabilities through catastrophic forgetting. We investigate which model layers drive this trade-off, an
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivLoongSage: A Production-Grade, High-Efficiency Agentic RL Framework for Frontier LLMs with Validated Recipes4harxivSEA-HELM Audio: Evaluating Multilingual ASR-LLM Pipeline Performance Across SEA-HELM Subsets4harxivCascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades4harxivN-Gram Memories at Two Bits: Quantizing Lookup Tables in Large Language Models4hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗