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

TL;DR

Large language models offer new forensic tools but also pose challenges by mimicking styles and generating synthetic texts, requiring forensic linguistics to adapt methods for legal and scientific credibility.

Contribution

The paper analyzes the dual impact of LLMs on forensic linguistics, highlighting limitations of current detection methods and proposing methodological adaptations.

Findings

LLMs can approximate stylistic features but differ from human writers.

Current AI-text detection methods face high false positives and adversarial vulnerabilities.

Forensic linguistics must adopt hybrid workflows and explainable detection to remain credible.

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.