Hybrid Large Language Model Inference: Balancing Cost and Quality through Intelligent Query Routing

Core Concepts

A hybrid inference approach that combines the strengths of large and small language models to save costs while maintaining response quality by intelligently routing queries to the appropriate model.

Abstract

The content discusses a hybrid inference approach for large language models (LLMs) that aims to balance inference costs and response quality. The key insights are: LLMs excel in most NLP tasks but require expensive cloud servers for deployment due to their large size. Smaller models that can run on lower-cost devices tend to have lower response quality. The proposed approach uses a router that assigns queries to the small or large model based on the predicted query difficulty and the desired quality level. This allows seamlessly trading quality for cost as per the scenario requirements. The router is designed to incorporate the non-deterministic nature of LLM responses and address challenges when the small model is significantly weaker than the large model. Experiments on a large benchmark dataset show that the approach can make up to 40% fewer calls to the large model with no drop in response quality, enabling cost-efficient LLM-backed experiences for both providers and consumers.

Stats

The average latency per query for the router is 0.036 ± 0.002 seconds, which is nearly 10x faster than the fastest LLM (FLAN-t5 (800m)) in the experiments.

Quotes

"Our approach uses a router that assigns queries to the small or large model based on the predicted query difficulty and the desired quality level." "The desired quality level can be tuned dynamically at test time to seamlessly trade quality for cost as per the scenario requirements." "In experiments our approach allows us to make up to 40% fewer calls to the large model, with no drop in response quality."

Key Insights Distilled From

Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

by Dujian Ding,... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14618.pdf

Deeper Inquiries

How can the router be extended to handle a larger number of LLM models beyond just a small and large model

To extend the router to handle a larger number of LLM models beyond just a small and large model, a few key techniques can be implemented: Ensemble Routing: Implementing an ensemble routing approach where the router can dynamically select from multiple LLM models based on the query characteristics. By training the router to evaluate the strengths and weaknesses of each model in the ensemble, it can intelligently route queries to the most suitable model. Hierarchical Routing: Introducing a hierarchical routing system where the router first categorizes queries based on certain features or criteria and then routes them to specific groups of LLM models. This hierarchical approach can handle a larger number of models efficiently by narrowing down the selection process at each level. Dynamic Thresholding: Utilizing dynamic thresholding techniques where the router adapts its decision thresholds based on the performance of each LLM model. This adaptive approach can help the router make more informed decisions when routing queries to a diverse set of models. Meta-Learning: Implementing meta-learning techniques to enable the router to quickly adapt to new LLM models by leveraging knowledge learned from previous model interactions. This can enhance the router's ability to generalize to a larger number of models without extensive retraining. By incorporating these techniques, the router can effectively handle a larger number of LLM models and optimize query routing based on the specific characteristics and capabilities of each model.

What techniques could be used to improve the router's ability to generalize to out-of-distribution data and model pairs

To improve the router's ability to generalize to out-of-distribution data and model pairs, the following techniques can be employed: Domain Adaptation: Implementing domain adaptation techniques to fine-tune the router on out-of-distribution data. By exposing the router to a diverse range of data sources during training, it can learn to generalize better to unseen data distributions. Transfer Learning: Leveraging transfer learning methods to pre-train the router on a diverse set of LLM model pairs. By transferring knowledge from related tasks or domains, the router can adapt more effectively to new model pairs without extensive retraining. Data Augmentation: Introducing data augmentation strategies to artificially increase the diversity of the training data. By generating variations of existing data samples, the router can learn to handle a wider range of scenarios and model pairs. Regularization Techniques: Applying regularization techniques such as dropout or weight decay to prevent overfitting and encourage the router to learn more robust representations that generalize well to out-of-distribution data. By incorporating these techniques, the router can enhance its ability to generalize to new data distributions and model pairs, improving its performance in diverse settings.

How can the router's performance be further improved by incorporating task-specific information beyond just the query text

To improve the router's performance by incorporating task-specific information beyond just the query text, the following strategies can be implemented: Task Embeddings: Introducing task embeddings that encode additional task-specific information into the routing process. By representing tasks as embeddings, the router can consider task context when making routing decisions, leading to more informed choices. Task Metadata: Including task metadata such as task type, domain, or complexity as additional input features to the router. By providing task-specific information alongside the query text, the router can tailor its routing decisions based on the unique characteristics of each task. Task-Specific Models: Training task-specific routers or incorporating task-specific modules within the router architecture. By customizing the routing process for different tasks, the router can optimize performance based on the specific requirements of each task. Multi-Modal Inputs: Integrating multi-modal inputs that combine text data with other modalities such as images, audio, or structured data related to the task. By considering a broader range of inputs, the router can make more contextually relevant routing decisions. By incorporating task-specific information beyond just the query text, the router can enhance its performance and adaptability to a wide range of tasks and scenarios.