Growing amount and quality of ai-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advan...
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A standard way to evaluate the abilities of LLM involves presenting a multiple-choice question and selecting the option with the highest logit as the model’s predicted answer. However, such a format for evaluating LL...
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We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets of two functions having a common domai...
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Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly imp...
Growing amount and quality of ai-generated texts makes detecting such content more difficult. In most real-world scenarios, the domain (style and topic) of generated data and the generator model are not known in advan...
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Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated. Detecting the boundary between human-written and...
Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly imp...
Rapidly increasing quality of ai-generated content makes it difficult to distinguish between human and ai-generated texts, which may lead to undesirable consequences for society. Therefore, it becomes increasingly important to study the properties of human texts that are invariant over different text domains and varying proficiency of human writers, can be easily calculated for any language, and can robustly separate natural and ai-generated texts regardless of the generation model and sampling method. In this work, we propose such an invariant for human-written texts, namely the intrinsic dimensionality of the manifold underlying the set of embeddings for a given text sample. We show that the average intrinsic dimensionality of fluent texts in a natural language is hovering around the value 9 for several alphabet-based languages and around 7 for Chinese, while the average intrinsic dimensionality of ai-generated texts for each language is ≈ 1.5 lower, with a clear statistical separation between human-generated and ai-generated distributions. This property allows us to build a score-based artificial text detector. The proposed detector's accuracy is stable over text domains, generator models, and human writer proficiency levels, outperforming SOTA detectors in model-agnostic and cross-domain scenarios by a significant margin. We release code and data ***/ArGintum/GPTID.
With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this prcoblem suffer from poor generalization across d...
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