# HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison Corpus

arXiv: https://arxiv.org/abs/2309.02731 · HTML: https://mlsanity.com/paper/2309.02731/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 23 references that are in the map.
- First posted on arXiv 6 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)

## Built on by (most cited first, 30 of 35)

- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [A Survey on Detection of LLMs-Generated Content](https://mlsanity.com/paper/2310.15654/) (2023)
- [Beemo: Benchmark of Expert-edited Machine-generated Outputs](https://mlsanity.com/paper/2411.04032/) (2024)
- [Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks](https://mlsanity.com/paper/2402.11638/) (2024)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization](https://mlsanity.com/paper/2403.01152/) (2024)
- [Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts](https://mlsanity.com/paper/2410.14677/) (2024)
- [textttDroid: A Resource Suite for AI-Generated Code Detection](https://mlsanity.com/paper/2507.10583/) (2025)
- [KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis](https://mlsanity.com/paper/2503.00032/) (2025)
- [Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency](https://mlsanity.com/paper/2603.12298/) (2026)
- [PADBen: A Comprehensive Benchmark for Evaluating AI Text Detectors Against Paraphrase Attacks](https://mlsanity.com/paper/2511.00416/) (2025)
- [Towards Reliable Detection of LLM-Generated Texts: A Comprehensive Evaluation Framework with CUDRT](https://mlsanity.com/paper/2406.09056/) (2024)
- [Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection](https://mlsanity.com/paper/2502.12611/) (2025)
- [The Arabic AI Fingerprint: Stylometric Analysis and Detection of Large Language Models Text](https://mlsanity.com/paper/2505.23276/) (2025)
- [CoCoNUTS: Concentrating on Content while Neglecting Uninformative Textual Styles for AI-Generated Peer Review Detection](https://mlsanity.com/paper/2509.04460/) (2025)
- [Minimizing Mismatch Risk: A Prototype-Based Routing Framework for Zero-shot LLM-generated Text Detection](https://mlsanity.com/paper/2602.01240/) (2026)
- [Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking](https://mlsanity.com/paper/2411.11736/) (2024)
- [The Statistical Signature of LLMs](https://mlsanity.com/paper/2602.18152/) (2026)
- [Spotlights and Blindspots: Evaluating Machine-Generated Text Detection](https://mlsanity.com/paper/2604.16607/) (2026)
- [Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators](https://mlsanity.com/paper/2605.03969/) (2026)
- [TSM-Bench: Detecting LLM-Generated Text in Real-World Wikipedia Editing Practices](https://mlsanity.com/paper/2605.31113/) (2026)
- [Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text Detection](https://mlsanity.com/paper/2406.16275/) (2024)
- [AI Text Detectors and the Misclassification of Slightly Polished Arabic Text](https://mlsanity.com/paper/2511.16690/) (2025)
- [MAGA-Bench: Machine-Augment-Generated Text via Alignment Detection Benchmark](https://mlsanity.com/paper/2601.04633/) (2026)
- [Reasoning-Aware AIGC Detection via Alignment and Reinforcement](https://mlsanity.com/paper/2604.19172/) (2026)
- [DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities](https://mlsanity.com/paper/2606.04205/) (2026)
- [Hitting a Moving Target: Test-Time Adaptation for AI Text Detection under Continual Distribution Shift](https://mlsanity.com/paper/2606.25152/) (2026)
- [Stylometry recognizes human and LLM-generated texts in short samples](https://mlsanity.com/paper/2507.00838/) (2025)
- [StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features](https://mlsanity.com/paper/2507.12064/) (2025)
- [Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability](https://mlsanity.com/paper/2607.04061/) (2026)

## Abstract (arXiv)

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.