# Generative AI Text Classification using Ensemble LLM Approaches

arXiv: https://arxiv.org/abs/2309.07755 · HTML: https://mlsanity.com/paper/2309.07755/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text](https://mlsanity.com/paper/2301.13852/) (2023)
- [A Short Survey of Viewing Large Language Models in Legal Aspect](https://mlsanity.com/paper/2303.09136/) (2023)
- [MarIA: Spanish Language Models](https://mlsanity.com/paper/2107.07253/) (2021)
- [BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling](https://mlsanity.com/paper/2207.06814/) (2022)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [Artificial Text Detection with Multiple Training Strategies](https://mlsanity.com/paper/2212.05194/) (2022)
- [DIALOG-22 RuATD Generated Text Detection](https://mlsanity.com/paper/2206.08029/) (2022)

## Built on by (most cited first, 29 of 29)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey](https://mlsanity.com/paper/2312.03014/) (2023)
- [Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains](https://mlsanity.com/paper/2309.11285/) (2023)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy](https://mlsanity.com/paper/2402.16041/) (2024)
- [An overview of model uncertainty and variability in LLM-based sentiment analysis. Challenges, mitigation strategies and the role of explainability](https://mlsanity.com/paper/2504.04462/) (2025)
- [CITING: Large Language Models Create Curriculum for Instruction Tuning](https://mlsanity.com/paper/2310.02527/) (2023)
- [A Simple yet Efficient Ensemble Approach for AI-generated Text Detection](https://mlsanity.com/paper/2311.03084/) (2023)
- [Examining the Prevalence and Dynamics of AI-Generated Media in Art Subreddits](https://mlsanity.com/paper/2410.07302/) (2024)
- [DART: An AIGT Detector using AMR of Rephrased Text](https://mlsanity.com/paper/2412.11517/) (2024)
- [Do Large Language Models Possess Sensitive to Sentiment?](https://mlsanity.com/paper/2409.02370/) (2024)
- [On the Effectiveness of Large Language Models in Automating Categorization of Scientific Texts](https://mlsanity.com/paper/2502.15745/) (2025)
- [101 Billion Arabic Words Dataset](https://mlsanity.com/paper/2405.01590/) (2024)
- [Evaluating ChatGPT on Nuclear Domain-Specific Data](https://mlsanity.com/paper/2409.00090/) (2024)
- [Classification of Safety Events at Nuclear Sites using Large Language Models](https://mlsanity.com/paper/2409.00091/) (2024)
- [Ensembling Finetuned Language Models for Text Classification](https://mlsanity.com/paper/2410.19889/) (2024)
- [Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring](https://mlsanity.com/paper/2502.05242/) (2025)
- [Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables](https://mlsanity.com/paper/2502.09073/) (2025)
- [RAL2M: Retrieval Augmented Learning-To-Match Against Hallucination in Compliance-Guaranteed Service Systems](https://mlsanity.com/paper/2601.02917/) (2026)
- [Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweets](https://mlsanity.com/paper/2406.17967/) (2024)
- [Mitigating Adversarial Attacks in LLMs through Defensive Suffix Generation](https://mlsanity.com/paper/2412.13705/) (2024)
- [Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reports](https://mlsanity.com/paper/2507.00742/) (2025)
- [LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification](https://mlsanity.com/paper/2512.10793/) (2025)
- [Paraphrasing Attack Resilience of Various AI-Generated Text Detection Methods](https://mlsanity.com/paper/2605.14240/) (2026)
- [Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Tasks](https://mlsanity.com/paper/2504.04277/) (2025)
- [The Impact of Role Design in In-Context Learning for Large Language Models](https://mlsanity.com/paper/2509.23501/) (2025)
- [Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models](https://mlsanity.com/paper/2604.23430/) (2026)
- [Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese](https://mlsanity.com/paper/2606.13991/) (2026)
- [Ensembles of Large Language Models for Identifying EQ-5D Studies in PubMed Based on Their Abstracts](https://mlsanity.com/paper/2606.19345/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown impressive performance across a variety of Artificial Intelligence (AI) and natural language processing tasks, such as content creation, report generation, etc. However, unregulated malign application of these models can create undesirable consequences such as generation of fake news, plagiarism, etc. As a result, accurate detection of AI-generated language can be crucial in responsible usage of LLMs. In this work, we explore 1) whether a certain body of text is AI generated or written by human, and 2) attribution of a specific language model in generating a body of text. Texts in both English and Spanish are considered. The datasets used in this study are provided as part of the Automated Text Identification (AuTexTification) shared task. For each of the research objectives stated above, we propose an ensemble neural model that generates probabilities from different pre-trained LLMs which are used as features to a Traditional Machine Learning (TML) classifier following it. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro F1 scores of 0.733 and 0.649) for English and Spanish texts, respectively. For the second task on model attribution, our model ranked in first place with macro F1 scores of 0.625 and 0.653 for English and Spanish texts, respectively.