mild-rgb/eli5-human-vs-ai-detector

ELI5 Human-vs-AI detector (ModernBERT-large)

Distilling Pangram would have been boring, and I wouldn't have learned anything.

A ModernBERT-large classifier fine-tuned to label a single document as human-written or AI-written, trained on the mild-rgb/eli5-human-vs-ai corpus (long-form r/explainlikeimfive answers versus same-question answers from seven 2026 models).

Read this before you use it

This is a real detector, but it detects a particular thing: how 2026 models write. It carries that into subject matter it has never seen, it is nearly blind to text from older models, and it is badly calibrated, so no single verdict from it should be trusted. It is published as a research artifact.

• In-domain it is perfect — balanced accuracy 1.000 on held-out data — but "in-domain" means ELI5-style Q&A, the seven specific 2026 generators it was trained on, under one generation prompt. That number is not a general claim and does not transfer. A bag-of-words logistic regression scores 0.9993 on the same easy in-domain data, so the in-domain number alone says little.

• It moves to a new domain, but not to an older generation of models. Put it on AI-written fiction, a domain it never saw, and it works: 83.8% of the AI text caught against an 11.3% false-positive rate on human fiction, balanced accuracy 0.863 and AUC 0.945, well clear of a bag-of-words baseline at 0.745 and 0.890. But that fiction was written by 2026 models, the same generation the detector trained on. Give it text from older models and it goes nearly blind:

  AI text from	this model misses	bag-of-words misses
  WildChat — GPT-4, GPT-3.5	94.6%	96.3%
  MAGE — LLaMA, OPT, davinci, and others	96.3%	93.8%
  RAID — llama-chat, MPT	90.4%	91.7%
  AI fiction — 2026 models	16.2%	43.4%

  On all three older-model sets it misses nearly everything and ties a trivial word-counting baseline. The fiction row is the only one written by 2026 models, and it is the only row where the detector clearly wins. So what it learned is what today's frontier models sound like. That knowledge crosses into new subject matter; it does not reach backwards to how machines wrote in 2022.

• It rarely mistakes human writing for AI, outside fiction. It flagged none of 240 Stack Exchange answers and 1.67% of 240 news articles. Human fiction is the exception, at roughly 9%, but the bag-of-words baseline flags nearly as much of it, which points at something about the fiction register rather than a fault peculiar to this model.

• Two caveats on the older-model numbers. Each of those three sets was scored on a 240-document sample (the fiction row is the full 364), so read them as clearly bad rather than precisely measured. And they change two things at once — a new domain and an older generator — so they answer "can it catch a model it has never seen," not "how well does it do on current models."

• Its confidence is untrustworthy on any single document. It pins most scores to 0 or 1 and makes confident mistakes: about 6% of human fiction is flagged at p >= 0.99, and adjacent passages of one continuous text flip between "certainly human" and "certainly machine." The aggregate accuracy is real; an individual high-confidence verdict is not reliable.

Do not use this model for consequential decisions about individuals — academic-integrity checks, moderation, hiring, or accusing anyone of using AI. On human fiction it flags about 11% of genuinely human writing as AI at the default 0.5 threshold, roughly one document in nine, and about 6% at near-certainty (p >= 0.99). A false accusation is a real harm. It is suitable for aggregate research and measurement, not for judging one person's document.

Intended use

Research into AI-text detection and its failure modes: reproducing the in-domain ceiling, studying the bag-of-words floor, and measuring what happens off distribution. That is what it is good for. It is not a general-purpose detector, and in particular it should not be pointed at text that may have been written by an older model, where it barely detects anything.

How to use

The model was trained on whitespace-normalized text (newline runs collapsed) with mean pooling (baked into the config). Normalize inputs the same way or results will drift.

import re, torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

name = "mild-rgb/eli5-human-vs-ai-detector"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForSequenceClassification.from_pretrained(name).eval()

def norm(t):                      # match training preprocessing
    return re.sub(r"\s+", " ", t).strip()

text = norm("your document here")
enc = tok(text, truncation=True, max_length=2048, return_tensors="pt")
with torch.no_grad():
    p_ai = model(**enc).logits.softmax(-1)[0, 1].item()
print(f"P(AI) = {p_ai:.3f}")      # label 1 = AI, 0 = human; threshold 0.5

Best on documents of roughly 250-800 words, the training length.

Training

• Base model: answerdotai/ModernBERT-large.
• Data: the mild-rgb/eli5-human-vs-ai corpus, question-grouped train/validation/test splits, all seven generators seen during training.
• Recipe: bf16, attn_implementation="sdpa", reference_compile=False, learning rate 3e-5, weight decay 8e-6, 2 epochs, mean pooling, gradient checkpointing on. Checkpoint selected on dev loss.
• This checkpoint is one seed of a five-seed run; all five reached AUC 1.000 in-domain with zero variance, so the seed choice is immaterial to the in-domain result.
• Full method, the bag-of-words floor, and the out-of-distribution probe are documented in the project's phase-3 write-up.

Limitations, in one line

A genuine detector of 2026-model writing. It carries that skill into new subject matter — a perfect 1.000 balanced accuracy in-domain, 0.863 on fiction it never saw — but not to text from older models, where it misses roughly nine documents in ten and ties a trivial baseline. It is also poorly calibrated: trust it in aggregate, never on one verdict.