ConspEmoLLM: Conspiracy Theory Detection using an Emotion-Based Large Language Model

The ConspEmoLLM Paper

News

📢 Mar. 29, 2024 We release the ConspEmoLLM and ConspLLM models and the codes!

Models

• ConspEmoLLM-7b
• ConspLLM-7b

Usage

You can use the models in your Python project with the Hugging Face Transformers library. Here is a simple example of how to load the model:

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map='auto')

Then follow the prompts in the paper to predict the results.

Generate

generate_ids = model.generate(inputs["input_ids"], max_length=256)
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True)[0]
print(response)

Batch inference. The data format needs to follow data/test.json.

bash src/run_inference.sh

Finetune

bash src/run_sft.sh

License

The ConspEmoLLM and ConspLLM are licensed under [MIT]. Please find more details in the MIT file.

Data

Raw data

Raw COCO data

Raw LOCOAnnotations

Citation

If you use the series of ConspEmoLLM in your work, please cite our paper:

@article{liu2024conspemollm,
  title={ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model},
  author={Liu, Zhiwei and Liu, Boyang and Thompson, Paul and Yang, Kailai and Jain, Raghav and Ananiadou, Sophia},
  journal={arXiv preprint arXiv:2403.06765},
  year={2024}
}