MUGC: Machine Generated versus User Generated Content Detection

• URL: http://arxiv.org/abs/2403.19725v1
• Date: Thu, 28 Mar 2024 07:33:53 GMT
• Title: MUGC: Machine Generated versus User Generated Content Detection
• Authors: Yaqi Xie, Anjali Rawal, Yujing Cen, Dixuan Zhao, Sunil K Narang, Shanu Sushmita,
• Abstract summary: We show that traditional methods demonstrate a high level of accuracy in identifying machine-generated data. Machine-generated texts tend to be shorter and exhibit less word variety compared to human-generated content. readability, bias, moral, and affect comparisons reveal a discernible contrast between machine-generated and human generated content.
• Score: 1.6602942962521352
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As advanced modern systems like deep neural networks (DNNs) and generative AI continue to enhance their capabilities in producing convincing and realistic content, the need to distinguish between user-generated and machine generated content is becoming increasingly evident. In this research, we undertake a comparative evaluation of eight traditional machine-learning algorithms to distinguish between machine-generated and human-generated data across three diverse datasets: Poems, Abstracts, and Essays. Our results indicate that traditional methods demonstrate a high level of accuracy in identifying machine-generated data, reflecting the documented effectiveness of popular pre-trained models like RoBERT. We note that machine-generated texts tend to be shorter and exhibit less word variety compared to human-generated content. While specific domain-related keywords commonly utilized by humans, albeit disregarded by current LLMs (Large Language Models), may contribute to this high detection accuracy, we show that deeper word representations like word2vec can capture subtle semantic variances. Furthermore, readability, bias, moral, and affect comparisons reveal a discernible contrast between machine-generated and human generated content. There are variations in expression styles and potentially underlying biases in the data sources (human and machine-generated). This study provides valuable insights into the advancing capacities and challenges associated with machine-generated content across various domains.

Related papers

• RKadiyala at SemEval-2024 Task 8: Black-Box Word-Level Text Boundary Detection in Partially Machine Generated Texts [0.0]
  This paper introduces few reliable approaches for identifying which part of a given text is machine generated at a word level. We present a comparison with proprietary systems, performance of our model on unseen domains' and generators' texts. The findings reveal significant improvements in detection accuracy along with comparison on other aspects of detection capabilities.
  arXiv  Detail & Related papers  (2024-10-22T03:21:59Z)
• Detecting Machine-Generated Long-Form Content with Latent-Space Variables [54.07946647012579]
  Existing zero-shot detectors primarily focus on token-level distributions, which are vulnerable to real-world domain shifts. We propose a more robust method that incorporates abstract elements, such as event transitions, as key deciding factors to detect machine versus human texts.
  arXiv  Detail & Related papers  (2024-10-04T18:42:09Z)
• Explaining Text Similarity in Transformer Models [52.571158418102584]
  Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers. We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
  arXiv  Detail & Related papers  (2024-05-10T17:11:31Z)
• Beyond Turing: A Comparative Analysis of Approaches for Detecting Machine-Generated Text [1.919654267936118]
  Traditional shallow learning, Language Model (LM) fine-tuning, and Multilingual Model fine-tuning are evaluated. Results reveal considerable differences in performance across methods. This study paves the way for future research aimed at creating robust and highly discriminative models.
  arXiv  Detail & Related papers  (2023-11-21T06:23:38Z)
• The Imitation Game: Detecting Human and AI-Generated Texts in the Era of ChatGPT and BARD [3.2228025627337864]
  We introduce a novel dataset of human-written and AI-generated texts in different genres. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text.
  arXiv  Detail & Related papers  (2023-07-22T21:00:14Z)
• 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)
• Paraphrase Detection: Human vs. Machine Content [3.8768839735240737]
  Human-authored paraphrases exceed machine-generated ones in terms of difficulty, diversity, and similarity. Transformers emerged as the most effective method across datasets with TF-IDF excelling on semantically diverse corpora.
  arXiv  Detail & Related papers  (2023-03-24T13:25:46Z)
• Vision+X: A Survey on Multimodal Learning in the Light of Data [64.03266872103835]
  multimodal machine learning that incorporates data from various sources has become an increasingly popular research area. We analyze the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions. We investigate the existing literature on multimodal learning from both the representation learning and downstream application levels.
  arXiv  Detail & Related papers  (2022-10-05T13:14:57Z)
• Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data [48.7576911714538]
  We discuss how these techniques can be used to detect political content across different platforms. We compare the performance of three groups of detection techniques relying on dictionaries, supervised machine learning, or neural networks. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by neural network- and machine-learning-based models.
  arXiv  Detail & Related papers  (2022-07-01T15:23:23Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

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.