metadata

license: openrail
widget:
  - text: I am totally a human, trust me bro.
    example_title: default
  - text: >-
      In Finnish folklore, all places and things, and also human beings, have a
      haltija (a genius, guardian spirit) of their own. One such haltija is
      called etiäinen—an image, doppelgänger, or just an impression that goes
      ahead of a person, doing things the person in question later does. For
      example, people waiting at home might hear the door close or even see a
      shadow or a silhouette, only to realize that no one has yet arrived.
      Etiäinen can also refer to some kind of a feeling that something is going
      to happen. Sometimes it could, for example, warn of a bad year coming. In
      modern Finnish, the term has detached from its shamanistic origins and
      refers to premonition. Unlike clairvoyance, divination, and similar
      practices, etiäiset (plural) are spontaneous and can't be induced. Quite
      the opposite, they may be unwanted and cause anxiety, like ghosts.
      Etiäiset need not be too dramatic and may concern everyday events,
      although ones related to e.g. deaths are common. As these phenomena are
      still reported today, they can be considered a living tradition, as a way
      to explain the psychological experience of premonition.
    example_title: real wikipedia
  - text: >-
      In Finnish folklore, all places and things, animate or inanimate, have a
      spirit or "etiäinen" that lives there. Etiäinen can manifest in many
      forms, but is usually described as a kind, elderly woman with white hair.
      She is the guardian of natural places and often helps people in need.
      Etiäinen has been a part of Finnish culture for centuries and is still
      widely believed in today. Folklorists study etiäinen to understand Finnish
      traditions and how they have changed over time.
    example_title: generated wikipedia
  - text: >-
      This paper presents a novel framework for sparsity-certifying graph
      decompositions, which are important tools in various areas of computer
      science, including algorithm design, complexity theory, and optimization.
      Our approach is based on the concept of "cut sparsifiers," which are
      sparse graphs that preserve the cut structure of the original graph up to
      a certain error bound. We show that cut sparsifiers can be efficiently
      constructed using a combination of spectral techniques and random
      sampling, and we use them to develop new algorithms for decomposing graphs
      into sparse subgraphs.
    example_title: from ChatGPT
  - text: >-
      Recent work has demonstrated substantial gains on many NLP tasks and
      benchmarks by pre-training on a large corpus of text followed by
      fine-tuning on a specific task. While typically task-agnostic in
      architecture, this method still requires task-specific fine-tuning
      datasets of thousands or tens of thousands of examples. By contrast,
      humans can generally perform a new language task from only a few examples
      or from simple instructions - something which current NLP systems still
      largely struggle to do. Here we show that scaling up language models
      greatly improves task-agnostic, few-shot performance, sometimes even
      reaching competitiveness with prior state-of-the-art fine-tuning
      approaches. Specifically, we train GPT-3, an autoregressive language model
      with 175 billion parameters, 10x more than any previous non-sparse
      language model, and test its performance in the few-shot setting. For all
      tasks, GPT-3 is applied without any gradient updates or fine-tuning, with
      tasks and few-shot demonstrations specified purely via text interaction
      with the model. GPT-3 achieves strong performance on many NLP datasets,
      including translation, question-answering, and cloze tasks, as well as
      several tasks that require on-the-fly reasoning or domain adaptation, such
      as unscrambling words, using a novel word in a sentence, or performing
      3-digit arithmetic. At the same time, we also identify some datasets where
      GPT-3's few-shot learning still struggles, as well as some datasets where
      GPT-3 faces methodological issues related to training on large web
      corpora. Finally, we find that GPT-3 can generate samples of news articles
      which human evaluators have difficulty distinguishing from articles
      written by humans. We discuss broader societal impacts of this finding and
      of GPT-3 in general.
    example_title: GPT-3 paper
datasets:
  - NicolaiSivesind/human-vs-machine
  - gfissore/arxiv-abstracts-2021
language:
  - en
pipeline_tag: text-classification
tags:
  - mgt-detection
  - ai-detection

Machine-generated text-detection by fine-tuning of language models

This project is related to a bachelor's thesis with the title "Turning Poachers into Gamekeepers: Detecting Machine-Generated Text in Academia using Large Language Models" (not yet published) written by Nicolai Thorer Sivesind and Andreas Bentzen Winje at the Department of Computer Science at the Norwegian University of Science and Technology.

It contains text classification models trained to distinguish human-written text from text generated by language models like ChatGPT and GPT-3. The best models were able to achieve an accuracy of 100% on real and GPT-3-generated wikipedia articles (4500 samples), and an accuracy of 98.4% on real and ChatGPT-generated research abstracts (3000 samples).

The dataset card for the dataset that was created in relation to this project can be found here.

NOTE: the hosted inference on this site only works for the RoBERTa-models, and not for the Bloomz-models. The Bloomz-models otherwise can produce wrong predictions when not explicitly providing the attention mask from the tokenizer to the model for inference. To be sure, the pipeline-library seems to produce the most consistent results.

Fine-tuned detectors

This project includes 12 fine-tuned models based on the RoBERTa-base model, and three sizes of the bloomz-models.

Base-model	RoBERTa-base	Bloomz-560m	Bloomz-1b7	Bloomz-3b
Wiki	roberta-wiki	Bloomz-560m-wiki	Bloomz-1b7-wiki	Bloomz-3b-wiki
Academic	roberta-academic	Bloomz-560m-academic	Bloomz-1b7-academic	Bloomz-3b-academic
Mixed	roberta-mixed	Bloomz-560m-mixed	Bloomz-1b7-mixed	Bloomz-3b-mixed

Datasets

The models were trained on selections from the GPT-wiki-intros and ChatGPT-Research-Abstracts, and are separated into three types, wiki-detectors, academic-detectors and mixed-detectors, respectively.

Wiki-detectors:
- Trained on 30'000 datapoints (10%) of GPT-wiki-intros.
- Best model (in-domain) is Bloomz-3b-wiki, with an accuracy of 100%.
Academic-detectors:
- Trained on 20'000 datapoints (100%) of ChatGPT-Research-Abstracts.
- Best model (in-domain) is Bloomz-3b-academic, with an accuracy of 98.4%
Mixed-detectors:
- Trained on 15'000 datapoints (5%) of GPT-wiki-intros and 10'000 datapoints (50%) of ChatGPT-Research-Abstracts.
- Best model (in-domain) is RoBERTa-mixed, with an F1-score of 99.3%.

Hyperparameters

All models were trained using the same hyperparameters:

{
 "num_train_epochs": 1,
 "adam_beta1": 0.9,
 "adam_beta2": 0.999,
 "batch_size": 8,
 "adam_epsilon": 1e-08
 "optim": "adamw_torch" # the optimizer (AdamW)
 "learning_rate": 5e-05, # (LR)
 "lr_scheduler_type": "linear", # scheduler type for LR
 "seed": 42, # seed for PyTorch RNG-generator.
}

Metrics

Metrics can be found at https://wandb.ai/idatt2900-072/IDATT2900-072.

In-domain performance of wiki-detectors:

Base model	Accuracy	Precision	Recall	F1-score
Bloomz-560m	0.973	*1.000	0.945	0.972
Bloomz-1b7	0.972	*1.000	0.945	0.972
Bloomz-3b	*1.000	*1.000	*1.000	*1.000
RoBERTa	0.998	0.999	0.997	0.998

In-domain peformance of academic-detectors:

Base model	Accuracy	Precision	Recall	F1-score
Bloomz-560m	0.964	0.963	0.965	0.964
Bloomz-1b7	0.946	0.941	0.951	0.946
Bloomz-3b	*0.984	*0.983	0.985	*0.984
RoBERTa	0.982	0.968	*0.997	0.982

F1-scores of the mixed-detectors on all three datasets:

Base model	Mixed	Wiki	CRA
Bloomz-560m	0.948	0.972	*0.848
Bloomz-1b7	0.929	0.964	0.816
Bloomz-3b	0.988	0.996	0.772
RoBERTa	*0.993	*0.997	0.829

Credits

GPT-wiki-intro, by Aaditya Bhat
arxiv-abstracts-2021, by Giancarlo
Bloomz, by BigScience
RoBERTa, by Liu et. al.

Citation

Please use the following citation:

@misc {sivesind_2023,
    author       = { {Nicolai Thorer Sivesind} and {Andreas Bentzen Winje} },
    title        = { Machine-generated text-detection by fine-tuning of language models },
    url          = { https://huggingface.co/andreas122001/roberta-academic-detector }
    year         = 2023,
    publisher    = { Hugging Face }
}