Smart: Scaling Down Language Models for Reduced Processing Fees

核心概念

Introducing Smart, a framework to minimize inference costs of Large Language Models while ensuring accuracy guarantees.

摘要

The article discusses the challenges of deploying high-performance Large Language Models (LLMs) due to increased costs. It introduces Smart, a framework that optimizes the tradeoff between accuracy and cost savings by profiling LLMs and strategically leveraging a mix of models. The profiling phase evaluates LLMs' accuracy, while the application phase processes remaining items using the most cost-efficient LLMs. Smart achieves significant cost savings compared to traditional models.

1. Introduction

   • High costs of deploying Large Language Models (LLMs).
   • Introduction of Smart framework for cost-effective inference.

2. Profiling Phase

   • Evaluates accuracy of LLMs through comparison with reference model.
   • Terminates profiling early if further evaluation is deemed wasteful.

3. Application Phase

   • Selects most cost-efficient LLM based on profiling results.
   • Processes remaining items using selected LLMs to meet accuracy constraints.

4. Smart-ModelMix

   • Combines multiple LLMs to maximize cost savings.
   • Partitioning items based on ratios for each model's processing.

5. Mixed Integer Linear Program (MILP)

   • Formulates optimization problem to minimize costs while ensuring accuracy constraints.
   • Utilizes predefined confidence levels and accuracy lower bounds for each LLM.

6. Expected Cost Calculation

   • Estimates expected costs of profiling additional items and processing remaining items.
   • Considers tradeoff between profiling overheads and application savings.

統計資料

Our experiments show up to 25.6× cost savings compared to GPT-4.
Smart achieves average cost savings of 7.2×, 4.2×, and 4.8× for different benchmarks.

引述

"We introduce Smart, Scaling Models Adaptively for Reduced Token Fees."
"Smart significantly reduces inference costs by leveraging a mix of LLMs."

從以下內容提煉的關鍵洞見

SMART

by Saehan Jo,Im... 於 arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13835.pdf

深入探究

How can Smart's approach be applied beyond language models

Smart's approach can be applied beyond language models in various fields where cost-effective inference with accuracy guarantees is essential. For example, in image recognition tasks, Smart could evaluate different models based on their performance and costs to minimize processing fees while ensuring accurate results. Similarly, in financial forecasting or healthcare diagnostics, Smart could help optimize the selection of predictive models to balance accuracy and cost-effectiveness. The framework's ability to profile multiple models and strategically combine them for inference can be beneficial across a wide range of AI applications.

What are potential drawbacks or limitations of relying on multiple models for inference

While relying on multiple models for inference offers potential benefits such as cost savings and improved accuracy through ensemble methods, there are also drawbacks and limitations to consider. One limitation is the increased complexity of managing multiple models, including integration challenges, version control issues, and maintenance overheads. Additionally, combining diverse models may introduce inconsistencies or biases that could impact the overall reliability of the system. Furthermore, using multiple models may require more computational resources and infrastructure support compared to using a single model.

How might the concept of accuracy guarantees in AI systems impact user trust and adoption

The concept of accuracy guarantees in AI systems can have a significant impact on user trust and adoption. By providing users with assurance that AI systems will deliver reliable results within specified confidence levels, accuracy guarantees enhance transparency and accountability in AI decision-making processes. This transparency fosters trust among users by offering insights into how decisions are made by AI systems. However, if not properly communicated or implemented effectively, inaccurate or misleading accuracy guarantees could lead to misplaced trust or skepticism from users regarding the system's capabilities. Therefore, clear communication about the limitations of these guarantees is crucial for building user confidence in AI technologies.