Who Writes the Review, Human or AI?

• URL: http://arxiv.org/abs/2405.20285v1
• Date: Thu, 30 May 2024 17:38:44 GMT
• Title: Who Writes the Review, Human or AI?
• Authors: Panagiotis C. Theocharopoulos, Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos,
• Abstract summary: This study proposes a methodology to accurately distinguish AI-generated and human-written book reviews. Our approach utilizes transfer learning, enabling the model to identify generated text across different topics. The experimental results demonstrate that it is feasible to detect the original source of text, achieving an accuracy rate of 96.86%.
• Score: 0.36498648388765503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing use of Artificial Intelligence in Natural Language Processing, concerns have been raised regarding the detection of AI-generated text in various domains. This study aims to investigate this issue by proposing a methodology to accurately distinguish AI-generated and human-written book reviews. Our approach utilizes transfer learning, enabling the model to identify generated text across different topics while improving its ability to detect variations in writing style and vocabulary. To evaluate the effectiveness of the proposed methodology, we developed a dataset consisting of real book reviews and AI-generated reviews using the recently proposed Vicuna open-source language model. The experimental results demonstrate that it is feasible to detect the original source of text, achieving an accuracy rate of 96.86%. Our efforts are oriented toward the exploration of the capabilities and limitations of Large Language Models in the context of text identification. Expanding our knowledge in these aspects will be valuable for effectively navigating similar models in the future and ensuring the integrity and authenticity of human-generated content.

Related papers

• DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive Learning [24.99797253885887]
  We argue that the key to accomplishing this task lies in distinguishing writing styles of different authors. We propose DeTeCtive, a multi-task auxiliary, multi-level contrastive learning framework. Our method is compatible with a range of text encoders.
  arXiv  Detail & Related papers  (2024-10-28T12:34:49Z)
• Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text [4.902089836908786]
  WhosAI is a triplet-network contrastive learning framework designed to predict whether a given input text has been generated by humans or AI. We show that our proposed framework achieves outstanding results in both the Turing Test and Authorship tasks.
  arXiv  Detail & Related papers  (2024-07-12T15:44:56Z)
• Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection [8.149808049643344]
  We propose a novel hybrid approach that combines TF-IDF techniques with advanced machine learning models. Our approach achieves superior performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-06-01T10:21:54Z)
• Detecting AI Generated Text Based on NLP and Machine Learning Approaches [0.0]
  Recent advances in natural language processing may enable AI models to generate writing that is identical to human written form in the future. This might have profound ethical, legal, and social repercussions. Our approach includes a machine learning methods that can differentiate between electronically produced text and human-written text.
  arXiv  Detail & Related papers  (2024-04-15T16:37:44Z)
• Evaluating the Efficacy of Hybrid Deep Learning Models in Distinguishing AI-Generated Text [0.0]
  My research investigates the use of cutting-edge hybrid deep learning models to accurately differentiate between AI-generated text and human writing. I applied a robust methodology, utilising a carefully selected dataset comprising AI and human texts from various sources, each tagged with instructions.
  arXiv  Detail & Related papers  (2023-11-27T06:26:53Z)
• Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey [97.33926242130732]
  Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. Despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text.
  arXiv  Detail & Related papers  (2023-10-23T18:11:32Z)
• Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy [52.765898203824975]
  We introduce a semantic-aware watermarking algorithm that considers the characteristics of conditional text generation and the input context. Experimental results demonstrate that our proposed method yields substantial improvements across various text generation models.
  arXiv  Detail & Related papers  (2023-07-25T20:24:22Z)
• MAGE: Machine-generated Text Detection in the Wild [82.70561073277801]
  Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection. We build a comprehensive testbed by gathering texts from diverse human writings and texts generated by different LLMs. Despite challenges, the top-performing detector can identify 86.54% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.
  arXiv  Detail & Related papers  (2023-05-22T17:13:29Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• A Latent-Variable Model for Intrinsic Probing [93.62808331764072]
  We propose a novel latent-variable formulation for constructing intrinsic probes. We find empirical evidence that pre-trained representations develop a cross-lingually entangled notion of morphosyntax.
  arXiv  Detail & Related papers  (2022-01-20T15:01:12Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)

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.