README.md

---
language: en
license: apache-2.0
tags:
- text-classification
- ai-generated-text-detection
- roberta
- adversarial-training
metrics:
- roc_auc
datasets:
- liamdugan/raid
---

# ADAL: AI-Generated Text Detection using Adversarial Learning

Adversarially trained AI-generated text detector based on the RADAR framework
([Hu et al., NeurIPS 2023](https://arxiv.org/abs/2307.03838)), extended with
a multi-evasion attack pool for robust detection.

## Overview
 
ADAL is an adversarially trained AI-generated text detector based on the RADAR framework (Hu et al., NeurIPS 2023), extended to the RAID benchmark with multi-generator training and a multi-evasion attack pool. The system trains a detector (RoBERTa-large) and a paraphraser (T5-base) in an adversarial game: the paraphraser learns to rewrite AI-generated text so it evades detection, while the detector learns to remain robust against those rewrites. The result is a detector that generalises across 11 AI generators and maintains high AUROC under five distinct evasion attacks.
 
Best result: **macro AUROC 0.9951** across all 11 RAID generators, robust to all attack types.

## Training

- **Base model**: `roberta-large`
- **Dataset**: [RAID](https://huggingface.co/datasets/liamdugan/raid) (Dugan et al., ACL 2024)
- **Evasion attacks seen during training**: t5_paraphrase, synonym_replacement, homoglyphs, article_deletion, misspelling
- **Best macro AUROC**: 0.9951
- **Generators**: chatgpt, gpt2, gpt3, gpt4, cohere, cohere-chat, llama-chat,
  mistral, mistral-chat, mpt, mpt-chat

## Architecture
 
```
RAID train split (attack='none')
        │
        ▼
   ┌────────────┐      ┌─────────────────────────────────┐
   │  xm (AI)   │─────▶│  Gσ — Paraphraser (T5-base)     │──▶ xp_ppo
   └────────────┘      │  ramsrigouthamg/t5_paraphraser  │
                       └─────────────────────────────────┘
                                        │
                              PPO reward R(xp, φ)
                                        │
   ┌────────────┐      ┌─────────────────────────────────┐
   │  xh (human)│─────▶│  Dϕ — Detector (RoBERTa-large)  │──▶ AUROC
   │  xm (AI)   │─────▶│  roberta-large                  │
   │  xp_ppo    │─────▶│  (trained via reweighted        │
   │  xp_det_k  │─────▶│   logistic loss)                │
   └────────────┘      └─────────────────────────────────┘
```

## Usage

```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch

tokenizer = RobertaTokenizer.from_pretrained("Shushant/ADAL_AI_Detector")
model     = RobertaForSequenceClassification.from_pretrained("Shushant/ADAL_AI_Detector")
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

## Author
 
**Shushanta Pudasaini **  
PhD Researcher, Technological University Dublin
Supervisors: Dr. Marisa Llorens Salvador · Dr. Luis Miralles-Pechuán · Dr. David Lillis