Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text

• URL: http://arxiv.org/abs/2401.09407v3
• Date: Wed, 3 Apr 2024 03:20:10 GMT
• Title: Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
• Authors: Mazal Bethany, Brandon Wherry, Emet Bethany, Nishant Vishwamitra, Anthony Rios, Peyman Najafirad,
• Abstract summary: 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.
• Score: 8.290557547578146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-generated text face two pertinent problems: First, they are severely limited in generalizing against real-world scenarios, where machine-generated text is produced by a variety of generators, including but not limited to GPT-4 and Dolly, and spans diverse domains, ranging from academic manuscripts to social media posts. Second, existing detection methodologies treat texts produced by LLMs through a restrictive binary classification lens, neglecting the nuanced diversity of artifacts generated by different LLMs. In this work, we undertake a systematic study on the detection of machine-generated text in real-world scenarios. We first study the effectiveness of state-of-the-art approaches and find that they are severely limited against text produced by diverse generators and domains in the real world. Furthermore, t-SNE visualizations of the embeddings from a pretrained LLM's encoder show that they cannot reliably distinguish between human and machine-generated text. Based on our findings, we introduce a novel system, T5LLMCipher, for detecting machine-generated text using a pretrained T5 encoder combined with LLM embedding sub-clustering to address the text produced by diverse generators and domains in the real world. We evaluate our approach across 9 machine-generated text systems and 9 domains and 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 compared to the top performing existing approaches and correctly attributes the generator of text with an accuracy of 93.6\%.

Related papers

• GigaCheck: Detecting LLM-generated Content [72.27323884094953]
  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.
  arXiv  Detail & Related papers  (2024-10-31T08:30:55Z)
• 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)
• ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination [1.8418334324753884]
  This paper introduces back-translation as a novel technique for evading detection. We present a model that combines these back-translated texts to produce a manipulated version of the original AI-generated text. We evaluate this technique on nine AI detectors, including six open-source and three proprietary systems.
  arXiv  Detail & Related papers  (2024-09-22T01:13:22Z)
• Spotting AI's Touch: Identifying LLM-Paraphrased Spans in Text [61.22649031769564]
  We propose a novel framework, paraphrased text span detection (PTD) PTD aims to identify paraphrased text spans within a text. We construct a dedicated dataset, PASTED, for paraphrased text span detection.
  arXiv  Detail & Related papers  (2024-05-21T11:22:27Z)
• 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)
• 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)
• 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)
• RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text [25.80571756447762]
  We present Real or Fake Text (RoFT), a website that invites users to try their hand at detecting machine-generated text. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.
  arXiv  Detail & Related papers  (2020-10-06T22:47:43Z)

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.