Robust and Fine-Grained Detection of AI Generated Texts
An ideal detection system for machine generated content is supposed to work
well on any generator as many more advanced LLMs come into existence day by
day. Existing systems often struggle with accurately identifying AI-generated
content over shorter texts. Further, not all texts might be entirely authored
by a human or LLM, hence we focused more over partial cases i.e human-LLM
co-authored texts. Our paper introduces a set of models built for the task of
token classification which are trained on an extensive collection of
human-machine co-authored texts, which performed well over texts of unseen
domains, unseen generators, texts by non-native speakers and those with
adversarial inputs. We also introduce a new dataset of over 2.4M such texts
mostly co-authored by several popular proprietary LLMs over 23 languages. We
also present findings of our models' performance over each texts of each domain
and generator. Additional findings include comparison of performance against
each adversarial method, length of input texts and characteristics of generated
texts compared to the original human authored texts.
6800646e0679d4ec4b9d01a7
Ram Mohan Rao Kadiyala
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2025-04-17T08:04:05.627Z
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Ram Kadiyala
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Siddartha Pullakhandam
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2025-04-17T08:18:54.938Z
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Siddartha Pullakhandam
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Kanwal Mehreen
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Drishti Sharma
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2025-04-17T08:19:22.573Z
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Drishti Sharma
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Siddhant Gupta
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2025-06-02T07:50:46.242Z
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Siddhant Gupta
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Jebish Purbey
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2025-04-17T08:23:00.066Z
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Jebish Purbey
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Ashay Srivastava
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2025-04-17T08:23:15.588Z
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Ashay Srivastava
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Subhasya TippaReddy
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2025-04-17T08:23:23.946Z
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Subhasya Tippareddy
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Arvind Reddy Bobbili
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Arvind Reddy Bobbili
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Suraj Telugara Chandrashekhar
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Modabbir Adeeb
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2025-04-17T08:23:46.461Z
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Modabbir Adeeb
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Srinadh Vura
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2025-04-17T08:23:53.315Z
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Srinadh Vura
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Hamza Farooq
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2025-05-15T10:33:25.230Z
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Hamza Farooq
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traversaal-ai
Robust and Fine-Grained Detection of AI Generated Texts
An ideal detection system for machine generated content is supposed to work
well on any generator as many more advanced LLMs come into existence day by
day. Existing systems often struggle with accurately identifying AI-generated
content over shorter texts. Further, not all texts might be entirely authored
by a human or LLM, hence we focused more over partial cases i.e human-LLM
co-authored texts. Our paper introduces a set of models built for the task of
token classification which are trained on an extensive collection of
human-machine co-authored texts, which performed well over texts of unseen
domains, unseen generators, texts by non-native speakers and those with
adversarial inputs. We also introduce a new dataset of over 2.4M such texts
mostly co-authored by several popular proprietary LLMs over 23 languages. We
also present findings of our models' performance over each texts of each domain
and generator. Additional findings include comparison of performance against
each adversarial method, length of input texts and characteristics of generated
texts compared to the original human authored texts.
A collection of models for token classification effectively identifies AI-generated content, including human-LLM co-authored texts, across various domains and generators, even with adversarial inputs.
token classification human-machine co-authored texts dataset adversarial inputs LLMs domains generators non-native speakers