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

arXiv: https://arxiv.org/abs/2512.06922 · HTML: https://mlsanity.com/paper/2512.06922/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 60 references that are in the map.
- First posted on arXiv 7 Dec 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [On the Reliability of Watermarks for Large Language Models](https://mlsanity.com/paper/2306.04634/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges](https://mlsanity.com/paper/2408.08946/) (2024)
- [Detecting AI-Generated Sentences in Human-AI Collaborative Hybrid Texts: Challenges, Strategies, and Insights](https://mlsanity.com/paper/2403.03506/) (2024)
- [Mark Your LLM: Detecting the Misuse of Open-Source Large Language Models via Watermarking](https://mlsanity.com/paper/2503.04636/) (2025)
- [CoheMark: A Novel Sentence-Level Watermark for Enhanced Text Quality](https://mlsanity.com/paper/2504.17309/) (2025)
- [Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement](https://mlsanity.com/paper/2410.14259/) (2024)
- [StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors](https://mlsanity.com/paper/2309.12810/) (2023)
- [SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphs](https://mlsanity.com/paper/2406.11239/) (2024)
- [Neural Authorship Attribution: Stylometric Analysis on Large Language Models](https://mlsanity.com/paper/2308.07305/) (2023)
- [Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authors](https://mlsanity.com/paper/2509.14543/) (2025)
- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [A Comprehensive Analysis of the Role of Artificial Intelligence and Machine Learning in Modern Digital Forensics and Incident Response](https://mlsanity.com/paper/2309.07064/) (2023)
- [GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method](https://mlsanity.com/paper/2403.07321/) (2024)
- [Authorship Attribution in Multilingual Machine-Generated Texts](https://mlsanity.com/paper/2508.01656/) (2025)
- [Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification](https://mlsanity.com/paper/2503.19099/) (2025)
- [I Can Find You in Seconds! Leveraging Large Language Models for Code Authorship Attribution](https://mlsanity.com/paper/2501.08165/) (2025)
- [A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution](https://mlsanity.com/paper/2410.21716/) (2024)
- [Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models](https://mlsanity.com/paper/2502.16857/) (2025)
- [Watermarking for AI Content Detection: A Review on Text, Visual, and Audio Modalities](https://mlsanity.com/paper/2504.03765/) (2025)
- [Watermarking Degrades Alignment in Language Models: Analysis and Mitigation](https://mlsanity.com/paper/2506.04462/) (2025)
- [Explainability of machine learning approaches in forensic linguistics: a case study in geolinguistic authorship profiling](https://mlsanity.com/paper/2404.18510/) (2024)
- [Better Call Claude: Can LLMs Detect Changes of Writing Style?](https://mlsanity.com/paper/2508.00680/) (2025)
- [Multi-Hierarchical Feature Detection for Large Language Model Generated Text](https://mlsanity.com/paper/2509.18862/) (2025)
- [One-shot Style Transfer LLM log-probabilities for Authorship Attribution and Verification](https://mlsanity.com/paper/2510.13302/) (2025)
- [Attribution Quality in AI-Generated Content:Benchmarking Style Embeddings and LLM Judges](https://mlsanity.com/paper/2510.13898/) (2025)

## Abstract (arXiv)

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.