# Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectors

arXiv: https://arxiv.org/abs/2503.15128 · HTML: https://mlsanity.com/paper/2503.15128/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [MAGE: Machine-generated Text Detection in the Wild](https://mlsanity.com/paper/2305.13242/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://mlsanity.com/paper/2301.12867/) (2023)
- [Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation](https://mlsanity.com/paper/2310.15515/) (2023)
- [Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News](https://mlsanity.com/paper/2009.07698/) (2020)
- [Deepfake Text Detection: Limitations and Opportunities](https://mlsanity.com/paper/2210.09421/) (2022)
- [Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains](https://mlsanity.com/paper/2309.11285/) (2023)
- [SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2404.14183/) (2024)
- [Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian](https://mlsanity.com/paper/2206.01583/) (2022)
- [Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect?](https://mlsanity.com/paper/2306.05871/) (2023)
- [MTG: A Benchmark Suite for Multilingual Text Generation](https://mlsanity.com/paper/2108.07140/) (2021)
- [SynSciPass: detecting appropriate uses of scientific text generation](https://mlsanity.com/paper/2209.03742/) (2022)

## Built on by (most cited first, 8 of 8)

- [FAID: Fine-Grained AI-Generated Text Detection Using Multi-Task Auxiliary and Multi-Level Contrastive Learning](https://mlsanity.com/paper/2505.14271/) (2025)
- [mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection](https://mlsanity.com/paper/2506.01702/) (2025)
- [CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages](https://mlsanity.com/paper/2509.26051/) (2025)
- [Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation](https://mlsanity.com/paper/2503.23242/) (2025)
- [Evaluation of Multilingual LLMs Personalized Text Generation Capabilities Targeting Groups and Social-Media Platforms](https://mlsanity.com/paper/2601.03752/) (2026)
- [mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code](https://mlsanity.com/paper/2604.21365/) (2026)
- [mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection](https://mlsanity.com/paper/2605.02695/) (2026)
- [mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection](https://mlsanity.com/paper/2605.02712/) (2026)

## Abstract (arXiv)

Since the proliferation of LLMs, there have been concerns about their misuse for harmful content creation and spreading. Recent studies justify such fears, providing evidence of LLM vulnerabilities and high potential of their misuse. Humans are no longer able to distinguish between high-quality machine-generated and authentic human-written texts. Therefore, it is crucial to develop automated means to accurately detect machine-generated content. It would enable to identify such content in online information space, thus providing an additional information about its credibility. This work addresses the problem by proposing a robust fine-tuning process of LLMs for the detection task, making the detectors more robust against obfuscation and more generalizable to out-of-distribution data.