Topic-Specific LoRA Adapters Show Clear Specialization Gains in Small NLP Test

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A small experiment tested whether two topic-specific LoRA adapters — one trained on guitar content, one on sourdough — could outperform a base language model on their respective domains. Results showed each adapter achieved its lowest perplexity on its own topic, confirming the intended specialization effect. Cross-topic performance also improved over the base model, though by a smaller margin, indicating partial generalization. The run included independent safety and adversarial checks, both of which passed, adding credibility beyond raw perplexity scores. A GPU memory constraint caused minor operational issues, but the overall findings were considered valid and modest in scope.

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