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[APSIPA ASC 2023] The official code of paper, "FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking".

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FactLLaMA

The official code of paper, "FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking", accepted for publication in APSIPA ASC 2023.

Datasets

The raw datasets can be downloaded at CofCED.

Checkpoints

The LORA checkpoints can be downloaded at here.

Acknowledgment

This project is built based on Alpaca-Lora.

Citation

Please cite our work if you found this project useful.

@INPROCEEDINGS{10317251,
  author={Cheung, Tsun-Hin and Lam, Kin-Man},
  booktitle={2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)}, 
  title={FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking}, 
  year={2023},
  volume={},
  number={},
  pages={846-853},
  doi={10.1109/APSIPAASC58517.2023.10317251}}

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[APSIPA ASC 2023] The official code of paper, "FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking".

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