Detect AI-generated text. Fingerprint which LLM wrote it. Open-source GPTZero alternative.
GPTZero charges $15/month. Originality.ai charges per scan. Turnitin locks you into institutional contracts.
lmscan is free, open-source, works offline, and tells you which model wrote the text.
$ lmscan "In today's rapidly evolving digital landscape, it's important
to note that artificial intelligence has become a pivotal force in
transforming how we navigate the complexities of modern life..."
🔍 lmscan v0.1.0 — AI Text Forensics
══════════════════════════════════════════════════
Verdict: 🤖 Likely AI (77% confidence)
Words: 184
Sentences: 10
Scanned in 0.01s
┌────────────────────────────┬──────────┬────────────────────┐
│ Feature │ Value │ Signal │
├────────────────────────────┼──────────┼────────────────────┤
│ Burstiness │ 0.07 │ 🔴 Very low (AI) │
│ Sentence length variance │ 0.27 │ 🟡 Below average │
│ Slop word density │ 20.7% │ 🔴 High (AI) │
│ Transition word ratio │ 2.2% │ 🟡 Elevated │
│ Readability consistency │ 0.00 │ 🔴 Very low (AI) │
│ ... │ │ │
└────────────────────────────┴──────────┴────────────────────┘
🔎 Model Attribution
1. GPT-4 / ChatGPT 62% — "delve", "tapestry", "beacon", "landscape" (×2), +19 more
2. Claude (Anthropic) 13% — "robust", "nuanced", "comprehensive"
3. Gemini (Google) 9% — "furthermore", "additionally"
⚠️ Flags
• Very low burstiness (0.07) — AI text is more uniform in complexity
• High slop word density (20.7%) — contains known AI vocabulary markers
pip install lmscanZero dependencies. Works with Python 3.9+. No API keys. No internet. No GPU.
# Scan text directly
lmscan "Your text here..."
# Scan a file
lmscan document.txt
# Pipe from stdin
cat essay.txt | lmscan -
# JSON output (for scripts and CI)
lmscan document.txt --format json
# Per-sentence breakdown
lmscan document.txt --sentences
# CI gate: fail if AI probability > 50%
lmscan submission.txt --threshold 0.5from lmscan import scan
result = scan("Text to analyze...")
print(f"AI probability: {result.ai_probability:.0%}")
print(f"Verdict: {result.verdict}")
print(f"Confidence: {result.confidence}")
# Which model wrote it?
for model in result.model_attribution:
print(f" {model.model}: {model.confidence:.0%}")
for evidence in model.evidence[:3]:
print(f" → {evidence}")
# Per-sentence analysis
for sentence in result.sentence_scores:
if sentence.ai_probability > 0.7:
print(f" 🤖 {sentence.text[:60]}... ({sentence.ai_probability:.0%})")from lmscan import scan_file
import glob
for path in glob.glob("submissions/*.txt"):
result = scan_file(path)
print(f"{path}: {result.verdict} ({result.ai_probability:.0%})")lmscan uses 12 statistical features derived from computational linguistics research to distinguish AI-generated text from human writing:
| Feature | What it measures | AI signal |
|---|---|---|
| Burstiness | Variance in sentence complexity | AI text is unusually uniform |
| Sentence length variance | How much sentence lengths vary | AI produces uniform lengths |
| Vocabulary richness | Type-token ratio (Yule's K corrected) | AI reuses words more |
| Hapax legomena ratio | Fraction of words appearing once | AI has fewer unique words |
| Zipf deviation | How word frequencies follow Zipf's law | AI deviates from natural distribution |
| Readability consistency | Flesch-Kincaid variance across paragraphs | AI maintains constant readability |
| Bigram/trigram repetition | Repeated word pairs and triples | AI repeats phrase structures |
| Transition word ratio | "however", "moreover", "furthermore"... | AI overuses transitions |
| Slop word density | Known AI vocabulary markers | "delve", "tapestry", "beacon"... |
| Punctuation entropy | Diversity of punctuation usage | AI is more predictable |
Each feature produces a signal via sigmoid transformation. The weighted combination produces the final AI probability.
lmscan includes vocabulary fingerprints for 5 major LLM families:
| Model | Distinctive markers |
|---|---|
| GPT-4 / ChatGPT | "delve", "tapestry", "landscape", "leverage", "multifaceted", "it's important to note" |
| Claude (Anthropic) | "certainly", "I'd be happy to", "straightforward", "I should note" |
| Gemini (Google) | "crucial", "here's a breakdown", "keep in mind" |
| Llama / Meta | "awesome", "fantastic", "hope this helps" |
| Mistral / Mixtral | "indeed", "moreover", "hence", "noteworthy" |
Attribution uses weighted vocabulary matching, phrase detection, and hedging pattern analysis.
What lmscan is good at:
- Detecting text with strong AI stylistic patterns
- Identifying which model family generated text
- Scanning at scale (thousands of documents) with zero cost
- Providing explainable evidence (not a black box)
What lmscan cannot do:
- Detect AI text that has been manually edited or paraphrased
- Work reliably on very short text (<50 words)
- Detect AI text in non-English languages (English-only for now)
- Replace human judgment — use as a signal, not a verdict
This is statistical analysis, not a neural classifier. It detects stylistic patterns, not watermarks. It works best on unedited LLM output and degrades gracefully on edited text.
- name: AI Content Check
run: |
pip install lmscan
lmscan submission.txt --threshold 0.7 --format jsonrepos:
- repo: https://github.com/stef41/lmscan
rev: v0.1.0
hooks:
- id: lmscan
args: ["--threshold", "0.7"]lmscan's approach is informed by published research on AI text detection:
- DetectGPT (Mitchell et al., 2023) — perturbation-based detection using log probability curvature
- GLTR (Gehrmann et al., 2019) — statistical visualization of token predictions
- Binoculars (Hans et al., 2024) — cross-model perplexity comparison
- Zipf's Law in NLP — word frequency distributions differ between human and AI text
- Stylometry — decades of authorship attribution research applied to AI forensics
lmscan takes the statistical intuitions from these papers and implements them as lightweight, dependency-free heuristics that work without requiring a reference language model.
Q: Is this as accurate as GPTZero? A: GPTZero uses neural classifiers trained on labeled data. lmscan uses statistical heuristics. GPTZero is more accurate on edge cases; lmscan is free, offline, and explainable. Use both if accuracy matters.
Q: Can students use this to evade AI detection? A: lmscan shows which features trigger detection, which could help someone understand why text reads as AI-generated. This is by design — understanding AI writing patterns makes everyone a better writer. The same information is available in published research papers.
Q: Does it work on non-English text? A: Currently English-only. The slop word lists and transition word lists are English-specific. Statistical features (entropy, burstiness) work across languages but haven't been calibrated.
Q: Does it phone home? A: No. Zero network requests. No telemetry. No API keys. Everything runs locally.
Q: How is model attribution possible without running the model? A: Each LLM family has characteristic vocabulary biases. GPT-4 loves "delve" and "tapestry". Claude says "I'd be happy to". These are statistical fingerprints — not guaranteed attribution, but strong signals.
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Apache-2.0