The week's pick
Reseach Article
Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 51 |
| Year of Publication: 2024 |
| Authors: Deepak Babu Piskala, Vijay Raajaa, Sachin Mishra, Bruno Bozza |
10.5120/ijca2024924172
|
Deepak Babu Piskala, Vijay Raajaa, Sachin Mishra, Bruno Bozza . Dynamic LLM Routing and Selection based on User Preferences: Balancing Performance, Cost, and Ethics. International Journal of Computer Applications. 186, 51 ( Nov 2024), 1-7. DOI=10.5120/ijca2024924172
Abstract
With the widespread deployment of large language models (LLMs) such as GPT-4 [12], BART [9], and LLaMA [5], the need for a system that can intelligently select the most suitable model for specific tasks—while balancing cost, latency, accuracy, and ethical considerations—has become increasingly important. Recognizing that not all tasks necessitate models with over 100+ billion parameters, we introduce OptiRoute, an advanced model routing engine designed to dynamically select and route tasks to the optimal LLM based on detailed user-defined requirements. OptiRoute captures both functional (e.g., accuracy, speed, cost) and non-functional (e.g., helpfulness, harmlessness, honesty) criteria, leveraging lightweight task analysis and complexity estimation to efficiently match tasks with the best-fit models from a diverse array of LLMs. By employing a hybrid approach combining k-nearest neighbors (kNN) search and hierarchical filtering, OptiRoute optimizes for user priorities while minimizing computational overhead. This makes it ideal for real-time applications in cloud-based ML platforms, personalized AI services, and regulated industries. [4]
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