---
license: mit
---
# PclGPT

PclGPT is a bilingual large language model group (LLM) based on ChatGLM-3 and LLaMA-2, divided into two versions according to the training language: PclGPT-CN (based on ChatGLM) and PclGPT-EN (based on LLaMA). Built upon these foundational models, PclGPT has undergone both pre-training and supervised fine-tuning (SFT) to detect patronizing and condescending language (PCL) and other offensive speech. The maximum supported context length for the model is 4096 tokens.

# Training Process

We constructed the Pcl-PT pre-training dataset and the Pcl-SFT supervised fine-tuning dataset for application in the pre-training and supervised fine-tuning processes.

# 🎓 This paper has been accepted in EMNLP 2024 (Findings)

Our paper can be accessed here. Paper link: [https://arxiv.org/abs/2410.00361](https://arxiv.org/abs/2410.00361)

Our github [https://github.com/dut-laowang/emnlp24-PclGPT](https://github.com/dut-laowang/emnlp24-PclGPT)

# Inference
After downloading the weights, use the following code for single-sample inference with PclGPT-CN.

```python
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True)
model = AutoModel.from_pretrained("DUTIR-Wang/PclGPT-CN", trust_remote_code=True).half().cuda()

def generate_response():
    # 样例文本
    sample_text = "区区扫地的，你可真是厉害坏了"
    
    instruction = (
        "假定你是一名语言学家，检测居高临下言论。居高临下言论是优势地位群体针对弱势群体的优越言论，"
        "语言攻击性较弱，但往往夹杂着讽刺，刻板印象，会对弱势群体造成伤害。"
        "居高临下根据语气程度分为 A级：非居高临下（正常非歧视、非冷暴力言论），"
        "B级：轻微居高临下（语气略带鼓励或怜悯），C级：中等居高临下（说话人较为客观陈述，但语气带有歧视），"
        "D级：严重居高临下（说话人语气轻蔑，严重歧视弱势群体）。"
        "接下来将给你一段文本，根据上述规则，你负责判断该文本属于（A/B/C/D级）的哪一级，并只回答选项。"
        "-> 文本：({})"
    ).format(sample_text)

    # Tokenizer 和模型推理
    inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_length=1024)
    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    model_output = extract_option(output_text)
    return classify_output(model_output)

def extract_option(output_text):
    options = ['A', 'B', 'C', 'D']
    for char in reversed(output_text.strip()):
        if char in options:
            return char
    return "无法识别的输出" 

def classify_output(model_output):
    # 根据模型输出的选项返回相应的解释
    if model_output == "A":
        return "判断为A级：非居高临下"
    elif model_output == "B":
        return "判断为B级：轻微居高临下"
    elif model_output == "C":
        return "判断为C级：中等居高临下"
    elif model_output == "D":
        return "判断为D级：严重居高临下"
    else:
        return "无法识别的输出，请检查输入或模型输出"

response = generate_response()
print(response)
```
The output will be
```
"判断为D级：严重居高临下"
```

# Cite
```bibtex
@misc{wang2024pclgptlargelanguagemodel,
      title={PclGPT: A Large Language Model for Patronizing and Condescending Language Detection}, 
      author={Hongbo Wang and Mingda Li and Junyu Lu and Hebin Xia and Liang Yang and Bo Xu and Ruizhu Liu and Hongfei Lin},
      year={2024},
      eprint={2410.00361},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.00361}, 
}
```
# Other Parts

For the PclGPT-EN model trained on English data, please check [https://huggingface.co./DUTIR-Wang/PclGPT-EN](https://huggingface.co./DUTIR-Wang/PclGPT-EN).