Towards Optimizing the Costs of LLM Usage (2402.01742v1)

Abstract: Generative AI and LLMs in particular are heavily used nowadays for various document processing tasks such as question answering and summarization. However, different LLMs come with different capabilities for different tasks as well as with different costs, tokenization, and latency. In fact, enterprises are already incurring huge costs of operating or using LLMs for their respective use cases. In this work, we propose optimizing the usage costs of LLMs by estimating their output quality (without actually invoking the LLMs), and then solving an optimization routine for the LLM selection to either keep costs under a budget, or minimize the costs, in a quality and latency aware manner. We propose a model to predict the output quality of LLMs on document processing tasks like summarization, followed by an LP rounding algorithm to optimize the selection of LLMs. We study optimization problems trading off the quality and costs, both theoretically and empirically. We further propose a sentence simplification model for reducing the number of tokens in a controlled manner. Additionally, we propose several deterministic heuristics for reducing tokens in a quality aware manner, and study the related optimization problem of applying the heuristics optimizing the quality and cost trade-off. We perform extensive empirical validation of our methods on not only enterprise datasets but also on open-source datasets, annotated by us, and show that we perform much better compared to closest baselines. Our methods reduce costs by 40%- 90% while improving quality by 4%-7%. We will release the annotated open source datasets to the community for further research and exploration.

• The paper proposes QC-Opt, a framework that leverages a BERTScore predictor and a budget-aware optimization algorithm to minimize LLM usage costs.
• It demonstrates a 40% to 90% cost reduction while achieving a 4% to 7% quality improvement compared to baseline approaches.
• The framework integrates token optimization strategies to reduce input lengths and maintain semantic integrity without compromising output quality.

Optimizing the Costs of LLM Usage

Introduction

The increasing reliance on LLMs in various document processing tasks has underscored the necessity to manage the associated costs and performance trade-offs. The diverse cost structures and latency inherent to different LLMs necessitate optimized model selection strategies that balance cost against task-specific performance metrics and latency constraints. This paper introduces a comprehensive framework, QC-Opt, aiming to optimize LLM usage by estimating output quality and implementing algorithmic solutions for model selection that minimize costs while maintaining desired quality levels.

QC-Opt Framework

QC-Opt consists of a multi-step process aimed at minimizing costs and optimizing input token lengths:

1. Quality Assessment: A model leverages a BERTScore predictor to estimate LLM output quality without model invocation.
2. Optimization Algorithm: A Budget Aware optimization algorithm selects suitable LLMs to maximize performance within budget and latency constraints.
3. Token Optimization Module: This module reduces input token lengths through controlled optimization, preserving output quality.

   Figure 1: QC-Opt: first, we have a BertScore predictor predicting the output quality of each LLM on each section; second, we have a Budget Aware optimization algorithm, that optimizes the LLM selection to maximize expected (predicted) performance subject to budget and latency constraints; third we have a token optimization module for reducing token length in a quality aware manner.

Model Selection and Routing

Budget-Aware Optimization

The core challenge lies in selecting the optimal LLM for processing each document section given a budget constraint. The proposed solution formulates this as a constrained optimization problem to maximize total expected quality subject to cost and latency constraints. Despite its inherent NP-hardness, efficient LP-rounding strategies and rudimentary greedy solutions for relaxed cases ensure practical applicability.

Performance and Cost Trade-Off

The framework empirically demonstrates substantial cost reductions (40% to 90%) with a quality increase of 4% to 7% over closest baselines. These improvements are achieved by intelligently distributing tasks among high-performance, cost-effective models and using token length optimizations.

Token Optimization Strategies

The Token Optimization process involves:

1. Text Simplification: Inspired by sentence simplification models, the framework rephrases input to reduce token count while preserving semantic content.
2. General Token Reduction Heuristics: A set of heuristics fine-tunes token lengths by adjusting spaces, capitalization, and applying lemmatization or synonym replacement, enhancing quality retention.

   Figure 2: Ablation study of various heuristics.

Empirical Evaluation

Extensive validation on datasets reflects the superiority of QC-Opt over traditional approaches such as FrugalGPT-inspired cascades, achieving comparable quality at markedly reduced costs. The quality predictor's alignment with human judgments in a user study reinforces its practical reliability.

Figure 3: Comparison with an LLM Cascade baseline inspired by FrugalGPT. We achieve same quality at considerably lower costs and latency (not shown here).

Conclusion

QC-Opt establishes a comprehensive framework for LLM cost optimization that balances performance metrics and cost constraints efficiently. Future extensions could further refine this framework to dynamically adapt LLM selections based on real-time contextual evaluations.

In the evolution of AI-driven document processing, the practical adaptability and efficiency of cost-optimization frameworks like QC-Opt could prove instrumental in maximizing both economic and computational resource utilization.