# Detecting undisclosed LLM-generated content in parliamentary texts

arXiv: https://arxiv.org/abs/2606.14209 · HTML: https://mlsanity.com/paper/2606.14209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2305.14902/) (2023)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Systematic Biases in LLM Simulations of Debates](https://mlsanity.com/paper/2402.04049/) (2024)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [Do LLMs write like humans? Variation in grammatical and rhetorical styles](https://mlsanity.com/paper/2410.16107/) (2024)
- [DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios](https://mlsanity.com/paper/2410.23746/) (2024)
- [Assessing Political Bias in Large Language Models](https://mlsanity.com/paper/2405.13041/) (2024)
- [Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text](https://mlsanity.com/paper/2506.07001/) (2025)
- [Vox Populi, Vox AI? Using Language Models to Estimate German Public Opinion](https://mlsanity.com/paper/2407.08563/) (2024)
- [Learning to Rewrite: Generalized LLM-Generated Text Detection](https://mlsanity.com/paper/2408.04237/) (2024)
- [Comparing LLM-generated and human-authored news text using formal syntactic theory](https://mlsanity.com/paper/2506.01407/) (2025)
- [Beyond Black Box AI-Generated Plagiarism Detection: From Sentence to Document Level](https://mlsanity.com/paper/2306.08122/) (2023)
- [Investigating Markers and Drivers of Gender Bias in Machine Translations](https://mlsanity.com/paper/2403.11896/) (2024)
- [Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction](https://mlsanity.com/paper/2412.15253/) (2024)
- [The AI Cognitive Trojan Horse: How Large Language Models May Bypass Human Epistemic Vigilance](https://mlsanity.com/paper/2601.07085/) (2026)
- [ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech](https://mlsanity.com/paper/2511.08247/) (2025)
- [Interpretable Text Classification Applied to the Detection of LLM-generated Creative Writing](https://mlsanity.com/paper/2601.07368/) (2026)

## Abstract (arXiv)

In this paper, we evaluate the extent of undisclosed LLM-generated content in texts from the parliaments of the United Kingdom and Sweden. In many areas, such as in journalism or in academic writing, there are often requirements to clearly disclose whether AI tools, such as LLMs, have been used. In the case of parliamentary texts, the guidelines on disclosure of AI use are more vague. However, in order to maintain transparency and retain public trust, it is generally recommended that parliamentarians should state whether or not they have used AI when writing texts, such as parliamentary motions. Here, we train an interpretable (glass-box) text classifier using pre-LLM parliamentary texts and LLM-generated versions of such texts. We then apply the classifier to a test set containing recent parliamentary texts, finding a steady increase in undisclosed LLM use, in both parliaments, from 2022 onwards.