3 papers
cs.DC2025
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Rickard Brüel-Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj +4
Fine-tuning large language models (LLMs) with low-rank adaptations (LoRAs) has become common practice, often yielding numerous copies of the same LLM differing only in their LoRA u…
stat.ML2025
CARROT: A Cost Aware Rate Optimal Router
Seamus Somerstep, Felipe Maia Polo, Allysson Flavio Melo de Oliveira +5
With the rapid growth in the number of Large Language Models (LLMs), there has been a recent interest in LLM routing, or directing queries to the cheapest LLM that can deliver a su…
cs.CL2024
Efficient multi-prompt evaluation of LLMs
Felipe Maia Polo, Ronald Xu, Lucas Weber +6
Most popular benchmarks for comparing LLMs rely on a limited set of prompt templates, which may not fully capture the LLMs' abilities and can affect the reproducibility of results…