Fine-Tuning Large Language Models for Digital Forensics: Case Study and General Recommendations
Gaëtan Michelet, Hans Henseler, Harm van Beek, Mark Scanlon, Frank Breitinger
Abstract
Large Language Models (LLMs) have rapidly gained popularity in various fields, including Digital Forensics (DF), where they offer the potential to accelerate investigative processes. Although several studies have explored LLMs for tasks such as evidence identification, artifact analysis, and report writing, fine-tuning models for specific forensic applications remains underexplored. This article addresses this gap by proposing recommendations for fine-tuning LLMs tailored to DF tasks. A case study on chat summarization is presented to showcase the applicability of the recommendations, where us evaluate multiple fine-tuned models to assess their performance. The study concludes with sharing the lessons learned from the case study.