People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
- URL: http://arxiv.org/abs/2501.15654v1
- Date: Sun, 26 Jan 2025 19:31:34 GMT
- Title: People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text
- Authors: Jenna Russell, Marzena Karpinska, Mohit Iyyer,
- Abstract summary: We hire annotators to read 300 non-fiction English articles and label them as either human-written or AI-generated.
Experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text.
We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.
- Score: 37.36534911201806
- License:
- Abstract: In this paper, we study how well humans can detect text generated by commercial LLMs (GPT-4o, Claude, o1). We hire annotators to read 300 non-fiction English articles, label them as either human-written or AI-generated, and provide paragraph-length explanations for their decisions. Our experiments show that annotators who frequently use LLMs for writing tasks excel at detecting AI-generated text, even without any specialized training or feedback. In fact, the majority vote among five such "expert" annotators misclassifies only 1 of 300 articles, significantly outperforming most commercial and open-source detectors we evaluated even in the presence of evasion tactics like paraphrasing and humanization. Qualitative analysis of the experts' free-form explanations shows that while they rely heavily on specific lexical clues ('AI vocabulary'), they also pick up on more complex phenomena within the text (e.g., formality, originality, clarity) that are challenging to assess for automatic detectors. We release our annotated dataset and code to spur future research into both human and automated detection of AI-generated text.
Related papers
- Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection [44.05134959039957]
We investigate how sociolinguistic attributes-gender, CEFR proficiency, academic field, and language environment-impact state-of-the-art AI text detectors.
Our results reveal significant biases: CEFR proficiency and language environment consistently affected detector accuracy, while gender and academic field showed detector-dependent effects.
These findings highlight the crucial need for socially aware AI text detection to avoid unfairly penalizing specific demographic groups.
arXiv Detail & Related papers (2025-02-18T07:49:31Z) - DAMAGE: Detecting Adversarially Modified AI Generated Text [0.13108652488669736]
We show that many existing AI detectors fail to detect humanized text.
We demonstrate a robust model that can detect humanized AI text while maintaining a low false positive rate.
arXiv Detail & Related papers (2025-01-06T23:43:49Z) - Detecting Machine-Generated Texts: Not Just "AI vs Humans" and Explainability is Complicated [8.77447722226144]
We introduce a novel ternary text classification scheme, adding an "undecided" category for texts that could be attributed to either source.
This research shifts the paradigm from merely classifying to explaining machine-generated texts, emphasizing need for detectors to provide clear and understandable explanations to users.
arXiv Detail & Related papers (2024-06-26T11:11:47Z) - 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) - 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) - Paraphrasing evades detectors of AI-generated text, but retrieval is an
effective defense [56.077252790310176]
We present a paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering.
Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking.
We introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider.
arXiv Detail & Related papers (2023-03-23T16:29:27Z) - Can AI-Generated Text be Reliably Detected? [50.95804851595018]
Large Language Models (LLMs) perform impressively well in various applications.
The potential for misuse of these models in activities such as plagiarism, generating fake news, and spamming has raised concern about their responsible use.
We stress-test the robustness of these AI text detectors in the presence of an attacker.
arXiv Detail & Related papers (2023-03-17T17:53:19Z)
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.