title: Robust and Fine-Grained Detection of AI Generated Texts
summary: 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.
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paper_title: Robust and Fine-Grained Detection of AI Generated Texts
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: token classification human-machine co-authored texts dataset adversarial inputs LLMs domains generators non-native speakers