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Related Experiment Video

Updated: Mar 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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LLM-based keyword augmentation for title-driven evidence selection: A practical approach.

Sanghyun Yoo1, Doowon Jeong1

  • 1Department of Forensic Sciences, Sungkyunkwan University, Seoul, South Korea.

Journal of Forensic Sciences
|March 13, 2026
PubMed
Summary

This study introduces a large language model (LLM) keyword augmentation method for digital forensic investigations. The approach enhances evidence discovery and reduces experience gaps in keyword-based searches.

Keywords:
file title‐based selectionkeyword augmentationkeyword‐based searchlarge language modelremote triage

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Area of Science:

  • Digital Forensics
  • Artificial Intelligence
  • Information Retrieval

Background:

  • Keyword-based search in digital forensics is experience-dependent, leading to inconsistent results.
  • Practical deployment of AI solutions is hindered by data confidentiality and infrastructure costs.

Purpose of the Study:

  • To develop a practical LLM-based keyword augmentation method for digital forensic evidence triage.
  • To enhance the effectiveness of keyword searches without compromising sensitive case data.

Main Methods:

  • Correlating file names with document bodies using semantic similarity and keyword coverage.
  • Evaluating prompt-only keyword generation from ChatGPT models on a benchmark dataset.
  • Conducting a usability study with digital forensic investigators to assess the impact of augmented keywords.

Main Results:

  • File names show a significant correlation with document content, distinct from random pairings.
  • ChatGPT models, particularly ChatGPT-4.1, demonstrated effective retrieval performance for keyword generation.
  • Augmented keywords significantly improved evidence detection, especially for junior investigators.

Conclusions:

  • The LLM-based keyword augmentation method enables efficient evidence triage using file names.
  • This approach supports large-scale and distributed investigations by reducing experience-related performance disparities.
  • The method offers a practical solution for enhancing digital forensic investigations while maintaining data privacy.