RouteLLM: Most Bang for Your Buck💥
So essentially,
RouteLLM enables cost reductions (up to 3.66x) compared to consistently using a high-cost model like GPT-4
Paper:
RouteLLM: Learning to Route LLMs with Preference Data (15 Pages)
Researchers from UC Berkeley, Anyscale and Canva are interested in optimizing the balance between cost and response quality with their language models.
Hmm..What’s the background?
Large language models (LLMs) are capable of carrying out a variety of natural language tasks, such as open-ended conversation, question answering, text summarization, and code generation. However, different LLMs vary in terms of their costs and sizes. Generally, larger models tend to be more capable but also more expensive, while smaller models are less capable but more cost-effective.
LLM routing, proposed by the researchers, offers a potential solution to this problem by dynamically selecting which LLM to use for a given query.
Ok, So what is proposed in the research paper?
RouteLLM proposes the use of router models to dynamically choose between a stronger, more expensive LLM (GPT-4) and a weaker, more affordable (Mixtral-8x7B) one. This dynamic selection aims to direct simpler queries to the weaker model and reserve the stronger model for more complex tasks.
The training framework for these router models utilizes human preference data, obtained from platforms like Chatbot Arena, and employs data augmentation techniques to enhance performance.
Evaluations on benchmarks like MMLU, MT Bench, and GSM8K reveal that RouteLLM can significantly reduce costs — by over 2 times in certain cases — without significantly compromising the quality of response.
What’s next?
For future work, the researchers suggest:
Expanding the framework to handle routing across multiple LLMs with varying capabilities and costs
Exploring methods to enhance router robustness to data distribution
Investigating techniques for optimizing the throughput of routers, particularly for those requiring GPUs, can further enhance the practicality and cost-effectiveness
So essentially,
RouteLLM enables cost reductions (up to 3.66x) compared to consistently using a high-cost model like GPT-4