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Found 3 papers, 1 papers with code
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.
LLM Performance Predictors are good initializers for Architecture Search
We show that HS-NAS performs very similar to SOTA NAS across benchmarks, reduces search hours by 50% roughly, and in some cases, improves latency, GFLOPs, and model size.
On Efficient Approximate Queries over Machine Learning Models
Alternatively, under the Core Set Closure assumption, we develop two algorithms: CSC that efficiently returns high quality answers with high probability and minimal oracle usage, and CSE, which extends it to more general settings.
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