Towards Optimizing the Costs of LLM Usage
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arXiv
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| Format: | Preprint |
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2024
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| author | Shekhar, Shivanshu Dubey, Tanishq Mukherjee, Koyel Saxena, Apoorv Tyagi, Atharv Kotla, Nishanth |
| author_facet | Shekhar, Shivanshu Dubey, Tanishq Mukherjee, Koyel Saxena, Apoorv Tyagi, Atharv Kotla, Nishanth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Optimizing the Costs of LLM Usage Shekhar, Shivanshu Dubey, Tanishq Mukherjee, Koyel Saxena, Apoorv Tyagi, Atharv Kotla, Nishanth Computation and Language Artificial Intelligence Machine Learning 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. |
| title | Towards Optimizing the Costs of LLM Usage |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.01742 |