LLM Augmented LLMs
Expand capabilities, improve efficiency
CommonProductProgrammingLanguage ModelProgramming
LLM Augmented LLMs achieve new capabilities by combining existing base models with more specific models. CALM (Composition to Augment Language Models) introduces cross-attention between models to combine their representations and achieve new capabilities. Its key advantages include:
(i) Scaling up LLMs on new tasks by "reusing" existing LLMs with a small amount of additional parameters and data;
(ii) Preserving the weights of existing models, therefore retaining their existing capabilities;
(iii) Applicability to different domains and settings. Experiments show that augmenting PaLM2-S with smaller models trained on low-resource languages resulted in absolute improvements of up to 13% on tasks such as English translation and arithmetic reasoning in low-resource languages. Similarly, when PaLM2-S was augmented with code-specific models, we saw up to 40% improvement in code generation and interpretation tasks compared to the base model, comparable to fully fine-tuned counterparts.
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