AI-generated Text Detection with a GLTR-based Approach

• URL: http://arxiv.org/abs/2502.12064v1
• Date: Mon, 17 Feb 2025 17:32:55 GMT
• Title: AI-generated Text Detection with a GLTR-based Approach
• Authors: Lucía Yan Wu, Isabel Segura-Bedmar,
• Abstract summary: Giant Language Model Test Room is a visual tool designed to help detect machine-generated texts based on GPT-2. One limitation of GLTR is that the results it returns can sometimes be ambiguous and lead to confusion. This study aims to explore various ways to improve GLTR's effectiveness for detecting AI-generated texts within the context of the IberLef-AuTexTification 2023 shared task.
• Score: 0.5524804393257919
• License:
• Abstract: The rise of LLMs (Large Language Models) has contributed to the improved performance and development of cutting-edge NLP applications. However, these can also pose risks when used maliciously, such as spreading fake news, harmful content, impersonating individuals, or facilitating school plagiarism, among others. This is because LLMs can generate high-quality texts, which are challenging to differentiate from those written by humans. GLTR, which stands for Giant Language Model Test Room and was developed jointly by the MIT-IBM Watson AI Lab and HarvardNLP, is a visual tool designed to help detect machine-generated texts based on GPT-2, that highlights the words in text depending on the probability that they were machine-generated. One limitation of GLTR is that the results it returns can sometimes be ambiguous and lead to confusion. This study aims to explore various ways to improve GLTR's effectiveness for detecting AI-generated texts within the context of the IberLef-AuTexTification 2023 shared task, in both English and Spanish languages. Experiment results show that our GLTR-based GPT-2 model overcomes the state-of-the-art models on the English dataset with a macro F1-score of 80.19%, except for the first ranking model (80.91%). However, for the Spanish dataset, we obtained a macro F1-score of 66.20%, which differs by 4.57% compared to the top-performing model.

Related papers

• Idiosyncrasies in Large Language Models [54.26923012617675]
  We unveil and study idiosyncrasies in Large Language Models (LLMs) We find that fine-tuning existing text embedding models on LLM-generated texts yields excellent classification accuracy. We leverage LLM as judges to generate detailed, open-ended descriptions of each model's idiosyncrasies.
  arXiv  Detail & Related papers  (2025-02-17T18:59:02Z)
• LuxVeri at GenAI Detection Task 1: Inverse Perplexity Weighted Ensemble for Robust Detection of AI-Generated Text across English and Multilingual Contexts [0.8495482945981923]
  This paper presents a system developed for Task 1 of the COLING 2025 Workshop on Detecting AI-Generated Content. Our approach utilizes an ensemble of models, with weights assigned according to each model's inverse perplexity, to enhance classification accuracy. Our results demonstrate the effectiveness of inverse perplexity weighting in improving the robustness of machine-generated text detection across both monolingual and multilingual settings.
  arXiv  Detail & Related papers  (2025-01-21T06:32:32Z)
• Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text [1.1137087573421256]
  This study aims to support efforts to detect and identify textual content generated using Generative AI Large Language Models. We leverage several machine learning algorithms such as Random Forest (RF), and Recurrent Neural Networks (RNN) to understand the important features in attribution. Our method is divided into 1) binary classification to differentiate between human-written and AI-text, and 2) multi classification, to differentiate between human-written text and the text generated by the five different LLM tools.
  arXiv  Detail & Related papers  (2025-01-06T18:46:53Z)
• Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore [51.65730053591696]
  We propose a simple but effective black-box zero-shot detection approach. It is predicated on the observation that human-written texts typically contain more grammatical errors than LLM-generated texts. Our method achieves an average AUROC of 98.7% and shows strong robustness against paraphrase and adversarial perturbation attacks.
  arXiv  Detail & Related papers  (2024-05-07T12:57:01Z)
• Native Language Identification with Large Language Models [60.80452362519818]
  We show that GPT models are proficient at NLI classification, with GPT-4 setting a new performance record of 91.7% on the benchmark11 test set in a zero-shot setting. We also show that unlike previous fully-supervised settings, LLMs can perform NLI without being limited to a set of known classes.
  arXiv  Detail & Related papers  (2023-12-13T00:52:15Z)
• A Simple yet Efficient Ensemble Approach for AI-generated Text Detection [0.5840089113969194]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in generating text that closely resembles human writing. It is essential to build automated approaches capable of distinguishing between artificially generated text and human-authored text. We propose a simple yet efficient solution by ensembling predictions from multiple constituent LLMs.
  arXiv  Detail & Related papers  (2023-11-06T13:11:02Z)
• Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models [0.0]
  Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models.
  arXiv  Detail & Related papers  (2023-10-16T14:33:02Z)
• Generative AI Text Classification using Ensemble LLM Approaches [0.12483023446237698]
  Large Language Models (LLMs) have shown impressive performance across a variety of AI and natural language processing tasks. We propose an ensemble neural model that generates probabilities from different pre-trained LLMs. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place.
  arXiv  Detail & Related papers  (2023-09-14T14:41:46Z)
• Smaller Language Models are Better Black-box Machine-Generated Text Detectors [56.36291277897995]
  Small and partially-trained models are better universal text detectors. We find that whether the detector and generator were trained on the same data is not critically important to the detection success. For instance, the OPT-125M model has an AUC of 0.81 in detecting ChatGPT generations, whereas a larger model from the GPT family, GPTJ-6B, has AUC of 0.45.
  arXiv  Detail & Related papers  (2023-05-17T00:09:08Z)
• On the Possibilities of AI-Generated Text Detection [76.55825911221434]
  We argue that as machine-generated text approximates human-like quality, the sample size needed for detection bounds increases. We test various state-of-the-art text generators, including GPT-2, GPT-3.5-Turbo, Llama, Llama-2-13B-Chat-HF, and Llama-2-70B-Chat-HF, against detectors, including oBERTa-Large/Base-Detector, GPTZero.
  arXiv  Detail & Related papers  (2023-04-10T17:47:39Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.