AI Text Detection Using Prefix Regeneration and N-Gram Similarity
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Solution Overview
Problem
The challenge of distinguishing between human-generated and artificial intelligence (AI)-generated text has become pressing due to the increasing fluency and diversity of large language models, with existing detection methods being inflexible, lacking interpretability, and failing to adapt to new models, especially when model details are not publicly available.
Innovation Solution
A method and system for detecting AI-generated text using a truncation module to extract a prefix and remainder text, employing AI text generation models to regenerate texts, and comparing n-gram similarities or model output probabilities to provide interpretable detection results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models generate text with increased fluency and diversity, then the quality and naturalness of machine-generated text is improved, but the difficulty of distinguishing AI-generated text from human-written text increases
Solution Approach 1:
The candidate text is divided into multiple segments (prefix text and remainder text) to enable localized analysis. The prefix text is used for regeneration while the remainder text is compared against regenerated versions, allowing the system to detect AI-generated content through segment-level n-gram similarity analysis rather than analyzing the entire text as a whole.
Solution Approach 2:
The system creates regenerated model texts by feeding the prefix text back into the language model to generate new versions. These copied/generated texts serve as reference points for comparison with the remainder text, enabling detection of patterns characteristic of AI-generated content through similarity analysis.
2Reliability
If existing detection methods are used, then detection capability is provided, but the methods lack interpretability and flexibility
Solution Approach 1:
The system provides feedback in the form of explanation texts that communicate the detection results and reasoning to users. By comparing n-gram similarities between regenerated texts and remainder text, the system generates interpretable explanations that feedback to users about why certain text is identified as AI-generated, enhancing transparency and trust.
Solution Approach 2:
The detection system is designed to be dynamic and adaptable to different language models. The method can be applied to various AI models without requiring retraining, as it operates by regenerating text through the same model and comparing patterns, making the system flexible and easy to operate across different model instances.
3Measurement precision
If detection methods are trained on specific model details, then detection accuracy for that model is improved, but the methods fail to adapt to new models when details are not publicly available
Solution Approach 1:
The detection method is designed to be universally applicable across different language models. Instead of training on model-specific details, the system uses a model-agnostic approach by regenerating text through the same model instance and comparing n-gram patterns, making it versatile and adaptable to any language model without requiring public model details or retraining.
Solution Approach 2:
The system performs self-service detection by using the language model itself to generate reference texts for comparison. Rather than relying on external training data or model internals, the method leverages the model's own generation capabilities to create comparison samples, enabling accurate detection that adapts automatically to different model versions.
Data Source
AI summary
Systems and methods for detecting artificial intelligence (AI) generated text. A candidate text can be truncated to obtain a prefix text and a remainder text by employing a truncation module. Regenerated model texts can be regenerated by utilizing the prefix text by employing an AI text generation model. Detection results can be predicted by comparing n-gram similarities of the regenerated model texts and the remainder text. The candidate text can be distinguished as AI generated text by providing explanation texts based on the detection results.