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" (see here) 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 }
}