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About this paper
Vytenis Šliogeris, Povilas Daniušis, Artūras Nakvosas
Full-parameter continual pretraining of the Gemma2 LLM using Elastic Weight Consolidation (EWC) enabled adaptation to Lithuanian without degrading English capabilities, preserving existing linguistic fluency and domain knowledge while enhancing both in the new language. The model showed improved performance across several Lithuanian language understanding benchmarks, retaining pre-existing mathematical abilities.
Key insights
- EWC effectively preserves both linguistic fluency (perplexity) and domain knowledge (benchmark accuracy) in existing languages while simultaneously enabling improvement in new languages.
- Enhancing linguistic fluency in a new language through continual pretraining can concurrently improve domain knowledge in that language.
- Regularization-based continual learning, specifically EWC, offers a viable and efficient pathway for adapting large LLMs to low-resource languages without needing access to the original vast training datasets.
Summary by alphaXiv.
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