许可证: MIT
语言:

• 英语
  基础模型:
• microsoft/deberta-v3-large
  任务标签: 文本分类

FactCG：用于检测大型语言模型无依据幻觉的事实核查模型

此模型来自我们的研究成果：

📃 FactCG：基于图的多跳数据增强事实核查 (NAACL2025, GitHub仓库)

可通过以下代码加载我们的模型：

from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification  
config = AutoConfig.from_pretrained("yaxili96/FactCG-DeBERTa-v3-Large", num_labels=2, finetuning_task="text-classification", revision='main', token=None, cache_dir="./cache")  
config.problem_type = "single_label_classification"  
tokenizer = AutoTokenizer.from_pretrained("yaxili96/FactCG-DeBERTa-v3-Large", use_fast=True, revision='main', token=None, cache_dir="./cache")  
model = AutoModelForSequenceClassification.from_pretrained(  
            "yaxili96/FactCG-DeBERTa-v3-Large", config=config, revision='main', token=None, ignore_mismatched_sizes=False, cache_dir="./cache")  

如果您认为该仓库或FactCG对您有所帮助，请引用以下论文：

@inproceedings{lei2025factcg,  
  title={FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data},  
  author={Lei, Deren and Li, Yaxi and Li, Siyao and Hu, Mengya and Xu, Rui and Archer, Ken and Wang, Mingyu and Ching, Emily and Deng, Alex},  
  journal={NAACL},  
  year={2025}  
}