Summary of Pickllm: Context-aware Rl-assisted Large Language Model Routing, by Dimitrios Sikeridis et al.
PickLLM: Context-Aware RL-Assisted Large Language Model Routing
by Dimitrios Sikeridis, Dennis Ramdass, Pranay Pareek
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent surge in open-source Large Language Models (LLMs) has created a diverse landscape regarding serving options and model expertise. However, users face challenges in efficiently optimizing LLM usage considering operational costs, efficiency, and specific measures like response accuracy, bias, or toxicity. Existing solutions focus on cost reduction, with optimizations relying on non-generalizable supervised training or ensemble approaches requiring output computation for every candidate LLM. To address this challenge, we propose PickLLM, a lightweight framework that uses Reinforcement Learning (RL) to route queries to available models. Our approach relies on a weighted reward function considering per-query costs, inference latency, and model response accuracy by a customizable scoring function. We explore two learning algorithms: gradient ascent for selecting a specific LLM or stateless Q-learning with an epsilon-greedy approach. The algorithm converges to a single LLM for remaining session queries. Our evaluation uses a pool of four LLMs and benchmark prompt-response datasets with different contexts, assessing response accuracy during the experiment. We demonstrate the speed of convergence for different learning rates and improvement in hard metrics like cost per querying session and overall response latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, there have been many new large language models that can be used right away. This has created a lot of options for how to use these models, but it’s not easy to find the best one for your specific task. Some existing solutions try to make this process more efficient by reducing costs or improving accuracy, but they don’t always work well in all situations. In this paper, we propose a new way to choose the right language model for your needs. We use a type of machine learning called reinforcement learning to find the best model based on how much it will cost and how accurate its responses are. Our approach is flexible and can be customized to fit different tasks and goals. We tested our method using four different language models and found that it worked well in most cases. |
Keywords
» Artificial intelligence » Inference » Language model » Machine learning » Prompt » Reinforcement learning » Supervised
