# conda

**Repository Path**: semikonductor/conda

## Basic Information

- **Project Name**: conda
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-25
- **Last Updated**: 2024-07-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ConDA-gen-text-detection
Code for the paper: **ConDA: Contrastive Domain Adaptation for AI-generated Text Detection** accepted at IJCNLP-AACL 2023 [paper link](https://arxiv.org/abs/2309.03992).

### :star2: Great News! [Nov 4, 2023] :star2: Our paper won the **Outstanding Paper Award** at IJCNLP-AACL 2023 held in Bali, Indonesia.



![ConDA Framework Diagram](https://github.com/AmritaBh/ConDA-gen-text-detection/blob/main/conda-framework.jpg)

## Setup

Set up a separate environment and install requirements via `pip install -r requirements.txt`

Make directories for the models, output logs and huggingface model files.

`mkdir models huggingface_repos output_logs`

Download `roberta-base` from [here](https://huggingface.co/roberta-base/tree/main) and/or `roberta-large` from [here](https://huggingface.co/roberta-large/tree/main) and place these repositories in `huggingface_repos`.

`contrast_training_with_da.py` is the ConDA training script. The `multi_domain_runner.py` is the runner script for training ConDA models. Update the arguments in `multi_domain_runner.py` to train models as needed. 

Use the `evaluation.py` script for evaluating models. Change arguments within the `evaluation.py` script as needed.

## TuringBench

Link to the dataset website: [link](https://turingbench.ist.psu.edu/)
Link to the TuringBench paper: [link](https://arxiv.org/abs/2109.13296)

Files should be split into 3 jsonl splits: train, valid, test. Each line in the jsonl is a data instance with `text` and `label` fields.

## Links to best performing models for each target generator

Here we provide links to pre-trained ConDA models for the best performing models:

| Target  | Best performing source | Dropbox Link |
| :-----------: | :-----------: | :-----: |
| CTRL  | GROVER_mega  | [link](https://www.dropbox.com/s/h5prhx3j4yndoig/grover_mega_ctrl_syn_rep_loss1.pt?dl=0) |
| FAIR_wmt19  | GPT2_xl  | [link](https://www.dropbox.com/s/h36fh24qu9203pf/gpt2_xl_fair_wmt19_syn_rep_loss1.pt?dl=0) |
| GPT2_xl | FAIR_wmt19  | [link](https://www.dropbox.com/s/mnx5lyg4geebhm6/fair_wmt19_gpt2_xl_syn_rep_loss1.pt?dl=0) |
| GPT3  | GROVER_mega  | [link](https://www.dropbox.com/s/mh09c8kdinocsz9/grover_mega_gpt3_syn_rep_loss1.pt?dl=0) |
| GROVER_mega  | CTRL  | [link](https://www.dropbox.com/s/o0fs8dodywvuda0/ctrl_grover_mega_syn_rep_loss1.pt?dl=0) |
| XLM  | GROVER_mega  | [link](https://www.dropbox.com/s/q6ddq2aop9qw8lo/grover_mega_xlm_syn_rep_loss1.pt?dl=0) |
| ChatGPT  | FAIR_wmt19  | [link](https://www.dropbox.com/s/sgwiucl1x7p7xsx/fair_wmt19_chatgpt_syn_rep_loss1.pt?dl=0) |

# Citation

If you use (part of) this code, please cite our paper as:

```
@InProceedings{bhattacharjee-EtAl:2023:ijcnlp,
  author    = {Bhattacharjee, Amrita  and  Kumarage, Tharindu  and  Moraffah, Raha  and  Liu, Huan},
  title     = {ConDA: Contrastive Domain Adaptation for AI-generated Text Detection},
  booktitle      = {Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics},
  month          = {November},
  year           = {2023},
  address        = {Nusa Dua, Bali},
  publisher      = {Association for Computational Linguistics},
  pages     = {598--610},
  url       = {https://aclanthology.org/2023.ijcnlp-long.40}
}
```

# Contact

For any questions, comments, and feedback, contact Amrita Bhattacharjee at abhatt43@asu.edu