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bd743a9 | 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 | """Fast-DetectGPT (Bao et al., 2023) — conditional probability curvature.
Analytic single-model form (sampling model = scoring model): using the model's full next-token
distribution we compute, per position, the observed log-prob vs its conditional mean and variance.
The standardized statistic d = (logp_obs - mu) / sigma is HIGH for machine text. We return d directly
(higher = more AI). Default proxy is gpt2 (Colab-cheap); use EleutherAI/gpt-neo-1.3B for a stronger
signal. arXiv:2310.05130
"""
from __future__ import annotations
import numpy as np
from .base import Detector
class FastDetectGPTDetector(Detector):
name = "fast-detectgpt"
def __init__(self, model_name: str = "gpt2", device: str | None = None, max_tokens: int = 512):
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
self.torch = torch
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.max_tokens = max_tokens
self.tok = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name).to(self.device).eval()
def _curvature(self, text: str) -> float:
torch = self.torch
if not text or not text.strip():
return float("nan")
ids = self.tok(text, return_tensors="pt", truncation=True,
max_length=self.max_tokens).input_ids.to(self.device)
if ids.size(1) < 3:
return float("nan")
with torch.no_grad():
logits = self.model(ids).logits[:, :-1, :] # predict tokens 1..L-1
lp = torch.log_softmax(logits, dim=-1) # [1, L-1, V]
p = lp.exp()
tgt = ids[:, 1:] # [1, L-1]
logp_tok = lp.gather(-1, tgt.unsqueeze(-1)).squeeze(-1) # observed log-probs
mu = (p * lp).sum(-1) # conditional mean (= -entropy)
var = (p * lp.pow(2)).sum(-1) - mu.pow(2) # conditional variance
num = (logp_tok - mu).sum()
den = var.sum().clamp_min(1e-8).sqrt()
d = (num / den).item()
return float(d)
def score(self, texts: list[str]) -> np.ndarray:
return np.array([self._curvature(t) for t in texts], dtype=float)
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