This paper has been accpected to LREC'22. Paper link: http://lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.744.pdf
conda create --name myenv python==3.6.8
source activate myenv
pip install -r requirements.txt
python -m spacy download zh_core_web_sm
python -m spacy download en_core_web_sm
install Torch with cuda 11.4:
pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
- We have open-sourced our datasets, which can be downloaded from https://drive.google.com/file/d/1xA9TtDYJE9BEwecL8QJ5d0LTytn5hhBr.
- Before running the experiment, you have to download the dataset and unzip all data to
data/folder - Label-1: The excerpt is written by Human. Label-0: The excerpt is written by Machine.
run app/train_on_en.sh for English Dataset and app/train_on_cn.sh for Chinese Dataset.
Example: bash train_on_en.sh en_grover '0' 1 1 0 1 1 'char_deduplicate'
Arguments for train_on_en.sh and train_on_cn.sh:
- dataset_name: Valid choices: ('en_grover', 'cn_novel_5billion', 'en_writing_prompt')
- char_freq_ranges: Use top-k characters from the Vocabulary
- is_change_apply_to_test: Whether apply Corruption/Replacement Operation to test set
- is_debug: if the training is in debug mode
- re_init_weights: Whether to use the pre-trained LM or initialize LM with random parameters
- repeat: The number of times experiment repeat for each Corruption/Replacement Operation setting.
- is_change_apply_to_train: Whether apply Corruption/Replacement Operation to training set
- semantic_change: Choices of Corruption/Replacement Operations
Valid choices of applying Corruption/Replacement Operation:
- reorder_shuffle: shuffle all tokens in an excerpt
- reorder_freq_high2low: sort tokens according to frequency (descending order)
- reorder_freq_low2high: sort tokens according to frequency (ascending order)
- char_deduplicate: remove duplicate tokens in an excerpt
- None: keep the original text
- likelihood_rank: replace text with Text Generation Models' Likelihood ranks
- pos: replace text with part-of-speech
- dep: replace text with dependency parsing tree
- constit: replace text with constituent parsing tree
- ner: replace text with Named Entities
- use stopword: keep only stop words
- non_use_stopword: keep only context words
run app/restrict_char_freq_region.sh
Example: bash restrict_char_freq_region.sh '10 20' 1 15 'rm_chars_in_freq' 'en_writing_prompt' 'en_roberta'
Arguments for restrict_char_freq_region.sh:
- char_freq_ranges: The dividing values for constraining tokens of certain frequencies, eg. (256, 512)
- is_debug: if the training is in debug mode
- repeat: The number of times experiment repeat for each frequency region
- semantic_change: Valid choice: ('rm_chars_in_freq', 'rm_chars_out_freq')
- dataset_name: Valid choices: ('en_grover', 'cn_novel_5billion', 'en_writing_prompt')
- classifier_name: If running for English Dataset, specify 'cn_roberta', if running for Chinese Dataset, specify 'cn_roberta'
run app/run_story_interpret.sh
Example: bash run_story_interpret.sh 50 1 256 en_grover interpret_en_grover_en_roberta_debug_1 roberta 50 1
Arguments for run_story_interpret.sh:
- debug_N: Number of Training sample, used for quick code tuning
- batch_size: Batch size for parameters update (Choose the one that fits your GPU memory)
- max_text_length: The maximum text length
- data_name: Valid choices: ('en_grover', 'cn_novel_5billion', 'en_writing_prompt')
- model_name: To interpret which model. Model should be trained with parameters saved in model_ckpts/$model_name. In this folder, 'config.json' and 'pytorch_model.bin' should be included.
- model_type: The model name, it could be roberta, bert, etc. It should corresponds to the model being trained.
- ig_n_steps: The number of steps used by the approximation method for Integrated Gradients.
- use_pad_baseline: Bool value. 1: use all [PAD] baseline for Integrated Gradients. 0: Use all zero baseline.
- To create result figures, you may refer to notebooks in result/vis
Our code is under Apache License 2.0.
If you found our paper/code useful, please cite:
@InProceedings{pu-EtAl:2022:LREC,
author = {Pu, Jiashu and Huang, Ziyi and Xi, Yadong and Chen, Guandan and Chen, Weijie and Zhang, Rongsheng},
title = {Unraveling the Mystery of Artifacts in Machine Generated Text},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {6889--6898},
abstract = {As neural Text Generation Models (TGM) have become more and more capable of generating text indistinguishable from human-written ones, the misuse of text generation technologies can have serious ramifications. Although a neural classifier often achieves high detection accuracy, the reason for it is not well studied. Most previous work revolves around studying the impact of model structure and the decoding strategy on ease of detection, but little work has been done to analyze the forms of artifacts left by the TGM. We propose to systematically study the forms and scopes of artifacts by corrupting texts, replacing them with linguistic or statistical features, and applying the interpretable method of Integrated Gradients. Comprehensive experiments show artifacts a) primarily relate to token co-occurrence, b) feature more heavily at the head of vocabulary, c) appear more in content word than stopwords, d) are sometimes detrimental in the form of number of token occurrences, e) are less likely to exist in high-level semantics or syntaxes, f) manifest in low concreteness values for higher-order n-grams.},
url = {https://aclanthology.org/2022.lrec-1.744}
}