File size: 3,543 Bytes
485127c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import transformers
import torch
import torch.nn.functional as F
import numpy as np
from data_builder import load_data, save_data
from metrics import get_roc_metrics, get_precision_recall_metrics
import argparse
import json
def load_detector(cache_dir, device):
# Load the RADAR detector model and tokenizer
detector_path_or_id = "TrustSafeAI/RADAR-Vicuna-7B"
print(f"load model and tokenizer: {detector_path_or_id}")
detector = transformers.AutoModelForSequenceClassification.from_pretrained(detector_path_or_id, cache_dir=cache_dir)
tokenizer = transformers.AutoTokenizer.from_pretrained(detector_path_or_id, cache_dir=cache_dir)
detector.eval()
detector.to(device)
return detector, tokenizer
def radar_ai_text_prob(Text_input, tokenizer, detector, device):
# Use detector to deternine wehther the text_input is ai-generated.
with torch.no_grad():
inputs = tokenizer(Text_input, padding=True, truncation=True, max_length=512, return_tensors="pt")
inputs = {k:v.to(device) for k,v in inputs.items()}
output_probs = F.log_softmax(detector(**inputs).logits,-1)[:,0].exp().tolist()
# output_probs is the probability that the input_text is generated by LLM.
# print("There are",len(Text_input),"input instances")
# print("Probability of AI-generated texts is", output_probs)
return output_probs
def experiment(args):
data = load_data(args.dataset_file)
detector, tokenizer = load_detector(args.cache_dir, args.device)
n_samples = len(data["sampled"])
# evaluate criterion
name = "radar"
torch.manual_seed(args.seed)
np.random.seed(args.seed)
Human_texts = data["original"]
human_preds = radar_ai_text_prob(Human_texts, tokenizer, detector, args.device)
Text_input = data["sampled"]
ai_preds = radar_ai_text_prob(Text_input, tokenizer, detector, args.device)
results = [{'original': data["original"][idx], 'original_crit': human_preds[idx], 'sampled': data["sampled"][idx], 'sampled_crit': ai_preds[idx]} for idx in range(n_samples)]
# compute prediction scores for real/sampled passages
predictions = {'real': [x["original_crit"] for x in results],
'samples': [x["sampled_crit"] for x in results]}
fpr, tpr, roc_auc = get_roc_metrics(predictions['real'], predictions['samples'])
p, r, pr_auc = get_precision_recall_metrics(predictions['real'], predictions['samples'])
print(f"Criterion {name}_threshold ROC AUC: {roc_auc:.4f}, PR AUC: {pr_auc:.4f}")
# results
results_file = f'{args.output_file}.{name}.json'
results = { 'name': f'{name}_threshold',
'info': {'n_samples': n_samples},
'predictions': predictions,
'raw_results': results,
'metrics': {'roc_auc': roc_auc, 'fpr': fpr, 'tpr': tpr},
'pr_metrics': {'pr_auc': pr_auc, 'precision': p, 'recall': r},
'loss': 1 - pr_auc}
with open(results_file, 'w') as fout:
json.dump(results, fout)
print(f'Results written into {results_file}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--output_file', type=str, default="./exp_main/results/xsum_gpt2-xl")
parser.add_argument('--dataset_file', type=str, default="./exp_main/data/xsum_gpt2-xl")
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--device', type=str, default="cuda")
parser.add_argument('--cache_dir', type=str, default="../cache")
args = parser.parse_args()
experiment(args)
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