Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI

• URL: http://arxiv.org/abs/2512.06922v1
• Date: Sun, 07 Dec 2025 17:05:31 GMT
• Title: Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI
• Authors: George Mikros,
• Abstract summary: Large language models (LLMs) serve as powerful analytical tools enabling scalable corpus analysis and embedding-based authorship attribution.<n>Recent stylometric research indicates that LLMs can approximate surface stylistic features yet exhibit detectable differences from human writers.<n>The article concludes that forensic linguistics requires methodological reconfiguration to remain scientifically credible and legally admissible.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) present a dual challenge for forensic linguistics. They serve as powerful analytical tools enabling scalable corpus analysis and embedding-based authorship attribution, while simultaneously destabilising foundational assumptions about idiolect through style mimicry, authorship obfuscation, and the proliferation of synthetic texts. Recent stylometric research indicates that LLMs can approximate surface stylistic features yet exhibit detectable differences from human writers, a tension with significant forensic implications. However, current AI-text detection techniques, whether classifier-based, stylometric, or watermarking approaches, face substantial limitations: high false positive rates for non-native English writers and vulnerability to adversarial strategies such as homoglyph substitution. These uncertainties raise concerns under legal admissibility standards, particularly the Daubert and Kumho Tire frameworks. The article concludes that forensic linguistics requires methodological reconfiguration to remain scientifically credible and legally admissible. Proposed adaptations include hybrid human-AI workflows, explainable detection paradigms beyond binary classification, and validation regimes measuring error and bias across diverse populations. The discipline's core insight, i.e., that language reveals information about its producer, remains valid but must accommodate increasingly complex chains of human and machine authorship.

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