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HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus

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arxiv 2309.02731 v4 pith:WMOO6QJL submitted 2023-09-06 cs.CL cs.AI

Zhenpeng Su , Xing Wu , Wei Zhou , Guangyuan Ma , Songlin Hu This is my paper · ORCID
classification cs.CLcs.AI
keywords taskssemantic-invariantchatgptdetectingtextchallengingfine-tuninginstruction
verification ladder T0 review T1 audit T2 compute T3 formal
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ChatGPT has garnered significant interest due to its impressive performance; however, there is growing concern about its potential risks, particularly in the detection of AI-generated content (AIGC), which is often challenging for untrained individuals to identify. Current datasets used for detecting ChatGPT-generated text primarily focus on question-answering tasks, often overlooking tasks with semantic-invariant properties, such as summarization, translation, and paraphrasing. In this paper, we demonstrate that detecting model-generated text in semantic-invariant tasks is more challenging. To address this gap, we introduce a more extensive and comprehensive dataset that incorporates a wider range of tasks than previous work, including those with semantic-invariant properties. In addition, instruction fine-tuning has demonstrated superior performance across various tasks. In this paper, we explore the use of instruction fine-tuning models for detecting text generated by ChatGPT.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability

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    Telescope Perplexity, the average negative log probability a reference LM assigns to each token immediately after seeing it, yields strong zero-shot LLM-text detection by probing an early-training aversion to repetition.

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    Adding human-alignment augmentation (roleplaying, BPO, self-refine, RLDF) to machine-generated text both fools existing detectors and improves the generalization of detectors fine-tuned on it.

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  4. Stylometry recognizes human and LLM-generated texts in short samples

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    Stylometric features and tree-based classifiers separate human-written Wikipedia summaries from LLM-generated texts with high cross-validated accuracy on a new seven-class benchmark, though performance drops on other ...

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    A non-neural stylometric detector using LightGBM on spaCy-derived features reached a final mean score of 0.897 on the PAN 2025 task, below the 0.922 TF-IDF SVM baseline.

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