Optimizing LLM Hyperparameters for Event Stream Analysis

EasyChair Preprint 15139

Abstract

Optimizing LLM Hyperparameters for Event Stream Analysis

Event stream analysis plays a pivotal role in real-time data processing across various domains, such as cybersecurity, finance, and IoT. Large Language Models (LLMs), with their ability to handle unstructured data and extract meaningful patterns, are increasingly being used for this purpose. However, optimizing LLM hyperparameters is crucial to achieving the balance between accuracy, latency, and resource efficiency required in real-time applications. This paper examines the key hyperparameters of LLMs—such as learning rate, batch size, sequence length, and model complexity—and their influence on performance in event stream environments. It explores various optimization techniques, including grid search, random search, Bayesian optimization, and evolutionary algorithms, highlighting their trade-offs and applicability in dynamic and resource-constrained systems. Through case studies in cybersecurity, financial monitoring, and IoT, the paper demonstrates the practical impact of hyperparameter tuning on real-time event stream processing. Additionally, it discusses future directions in adaptive and scalable hyperparameter optimization to enhance the efficiency of LLMs in increasingly complex event streams.

Keyphrases: Event Stream Analysis, Hyperparameter Optimization, Large Language Models (LLMs), machine learning

@booklet{EasyChair:15139,
  author    = {Docas Akinyele},
  title     = {Optimizing LLM Hyperparameters for Event Stream Analysis},
  howpublished = {EasyChair Preprint 15139},
  year      = {EasyChair, 2024}}