  As the dependence on computer systems expands across various domains,
focusing on personal, industrial, and large-scale applications, there arises a
compelling need to enhance their reliability to sustain business operations
seamlessly and ensure optimal user satisfaction. System logs generated by these
devices serve as valuable repositories of historical trends and past failures.
The use of machine learning techniques for failure prediction has become
commonplace, enabling the extraction of insights from past data to anticipate
future behavior patterns. Recently, large language models have demonstrated
remarkable capabilities in tasks including summarization, reasoning, and event
prediction. Therefore, in this paper, we endeavor to investigate the potential
of large language models in predicting system failures, leveraging insights
learned from past failure behavior to inform reasoning and decision-making
processes effectively. Our approach involves leveraging data from the Intel
Computing Improvement Program (ICIP) system crash logs to identify significant
events and develop CrashEventLLM. This model, built upon a large language model
framework, serves as our foundation for crash event prediction. Specifically,
our model utilizes historical data to forecast future crash events, informed by
expert annotations. Additionally, it goes beyond mere prediction, offering
insights into potential causes for each crash event. This work provides the
preliminary insights into prompt-based large language models for the log-based
event prediction task.
