Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference

Large Language Models (LLMs) like GPT-3 have sparked significant interest in their generative capabilities, leading to the development of various commercial applications. The high cost of using the models drives application builders to maximize the value of generation under a limited inference budget. This paper presents a study of optimizing inference hyperparameters like the number of responses, temperature and max tokens, which significantly affects the utility/cost of text generation. We design a framework named EcoOptiGen which leverages economical hyperparameter optimization and cost-based pruning. Experiments with the latest GPT-3.5 models on a variety of tasks verify its effectiveness. EcoOptiGen is implemented in the FLAML library (opens in new tab).