GigaCheck: Detecting LLM-generated Content

• URL: http://arxiv.org/abs/2410.23728v1
• Date: Thu, 31 Oct 2024 08:30:55 GMT
• Title: GigaCheck: Detecting LLM-generated Content
• Authors: Irina Tolstykh, Aleksandra Tsybina, Sergey Yakubson, Aleksandr Gordeev, Vladimir Dokholyan, Maksim Kuprashevich,
• Abstract summary: In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize artificially generated intervals within text.
• Score: 72.27323884094953
• License:
• Abstract: With the increasing quality and spread of LLM-based assistants, the amount of artificially generated content is growing rapidly. In many cases and tasks, such texts are already indistinguishable from those written by humans, and the quality of generation tends to only increase. At the same time, detection methods are developing more slowly, making it challenging to prevent misuse of these technologies. In this work, we investigate the task of generated text detection by proposing the GigaCheck. Our research explores two approaches: (i) distinguishing human-written texts from LLM-generated ones, and (ii) detecting LLM-generated intervals in Human-Machine collaborative texts. For the first task, our approach utilizes a general-purpose LLM, leveraging its extensive language abilities to fine-tune efficiently for the downstream task of LLM-generated text detection, achieving high performance even with limited data. For the second task, we propose a novel approach that combines computer vision and natural language processing techniques. Specifically, we use a fine-tuned general-purpose LLM in conjunction with a DETR-like detection model, adapted from computer vision, to localize artificially generated intervals within text. We evaluate the GigaCheck on five classification datasets with English texts and three datasets designed for Human-Machine collaborative text analysis. Our results demonstrate that GigaCheck outperforms previous methods, even in out-of-distribution settings, establishing a strong baseline across all datasets.

Related papers

• CUDRT: Benchmarking the Detection of Human vs. Large Language Models Generated Texts [10.027843402296678]
  This paper constructs a comprehensive benchmark in both Chinese and English to evaluate mainstream AI-generated text detectors. We categorize text generation into five distinct operations: Create, Update, Delete, Rewrite, and Translate. For each CUDRT category, we have developed extensive datasets to thoroughly assess detector performance.
  arXiv  Detail & Related papers  (2024-06-13T12:43:40Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Exploration of Masked and Causal Language Modelling for Text Generation [6.26998839917804]
  This paper conducts an extensive comparison of Causal Language Modelling approaches for text generation tasks. We first employ quantitative metrics and then perform a qualitative human evaluation to analyse coherence and grammatical correctness. The results show that consistently outperforms CLM in text generation across all datasets.
  arXiv  Detail & Related papers  (2024-05-21T09:33:31Z)
• LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning [4.328134379418151]
  Existing AI-generated text detection models are prone to in-domain over-fitting. We propose LLM-Detector, a novel method for both document-level and sentence-level text detection.
  arXiv  Detail & Related papers  (2024-02-02T05:54:12Z)
• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text [98.28130949052313]
  A score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. We propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data.
  arXiv  Detail & Related papers  (2024-01-22T16:09:47Z)
• Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text [8.290557547578146]
  We introduce a novel system, T5LLMCipher, for detecting machine-generated text using a pretrained T5 encoder combined with LLM embedding sub-clustering. We find that our approach provides state-of-the-art generalization ability, with an average increase in F1 score on machine-generated text of 19.6% on unseen generators and domains.
  arXiv  Detail & Related papers  (2024-01-17T18:45:13Z)
• A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions [39.36381851190369]
  There is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content. The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, statistics-based detectors, neural-base detectors, and human-assisted methods.
  arXiv  Detail & Related papers  (2023-10-23T09:01:13Z)
• SeqXGPT: Sentence-Level AI-Generated Text Detection [62.3792779440284]
  We introduce a sentence-level detection challenge by synthesizing documents polished with large language models (LLMs) We then propose textbfSequence textbfX (Check) textbfGPT, a novel method that utilizes log probability lists from white-box LLMs as features for sentence-level AIGT detection.
  arXiv  Detail & Related papers  (2023-10-13T07:18:53Z)
• LLMDet: A Third Party Large Language Models Generated Text Detection Tool [119.0952092533317]
  Large language models (LLMs) are remarkably close to high-quality human-authored text. Existing detection tools can only differentiate between machine-generated and human-authored text. We propose LLMDet, a model-specific, secure, efficient, and extendable detection tool.
  arXiv  Detail & Related papers  (2023-05-24T10:45:16Z)
• 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)

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.