LLMSYS-HPOBench: Hyperparameter Optimization Benchmark Suite for Real-World LLM Systems
For the AutoML community, this provides a much-needed benchmark for HPO on complex LLM systems, addressing gaps in existing benchmarks.
The paper introduces LLMSYS-HPOBench, the first benchmark suite for hyperparameter optimization of real-world LLM systems, containing 364,450 configurations with up to 23 hyperparameters and multiple fidelity factors and metrics. It aims to enable revalidation of existing HPO algorithms and foster new research directions.
Large Language Model (LLM) systems have been the frontier of AI in many application domains, leading to new challenges and opportunities for hyperparameter optimization (HPO) for the AutoML community. However, this type of system exhibits an unprecedented compound space of hyperparameter configuration from both the AI and non-AI components; rich and nonlinear implications from the fidelity factors; and diverse costs of measuring hyperparameter configurations, none of which have been fully captured in existing benchmarks. This paper presents the first (live) benchmark suite and datasets for HPO of real-world LLM systems, dubbed LLMSYS-HPOBench, covering data related to the inference objective values of hyperparameter configurations profiled from running the LLM systems. Currently, LLMSYS-HPOBench contains 364,450 hyperparameter configurations with a dimensionality of 12-23, 3-5 dimensions of fidelity factor leading to 932 settings, 3-9 inference objective metrics, and 2-10 cost metrics, together with generated logs from measuring the LLM systems. What we seek to advocate is not only a revalidation of the existing HPO algorithms over the frontier LLM systems, but also to provide an evolving platform for the AutoML community to explore new directions of research in this regard. The benchmark suite has been made available at: https://github.com/ideas-labo/llmsys-hpobench