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Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower cost (e. g., edge) devices, tend to lag behind in terms of response quality.
Hybrid Retrieval-Augmented Generation for Real-time Composition Assistance
Retrieval augmentation enhances performance of traditional language models by incorporating additional context.
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