ADAL Detector (RoBERTa-large)
Adversarially trained AI-generated text detector based on the RADAR framework (Hu et al., NeurIPS 2023), extended with a multi-evasion attack pool for robust detection.
Training
- Base model:
roberta-large - Dataset: RAID (Dugan et al., ACL 2024)
- Evasion attacks seen during training: t5_paraphrase, synonym_replacement, homoglyphs, article_deletion, misspelling
- Best macro AUROC: 0.9940
- Generators: chatgpt, gpt2, gpt3, gpt4, cohere, cohere-chat, llama-chat, mistral, mistral-chat, mpt, mpt-chat
Usage
from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch
tokenizer = RobertaTokenizer.from_pretrained("Shushant/ADAL-detector-large")
model = RobertaForSequenceClassification.from_pretrained("Shushant/ADAL-detector-large")
model.eval()
text = "Your text here."
enc = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
probs = torch.softmax(model(**enc).logits, dim=-1)[0]
print(f"P(human)={probs[1]:.3f} P(AI)={probs[0]:.3f}")
Label mapping
- Index 0 → AI-generated
- Index 1 → Human-written