Fake-review Rs-QLoRA adapters
Paper artifact for a controlled study of genuine human reviews versus reviews
generated by GPT-2-era, LFM2.5-1.2B, DeepSeek-v4-pro, and GLM-5.2 systems.
This repository contains the saved PEFT adapters, per-run configuration and
metrics, the complete experiment ledger, and a cryptographic manifest. It does
not contain the quantized Qwen2.5 base model.
Principal result
Matched-generator recall is 99.3%, but mean transfer recall across all four
generators is 39.3%. The apparent contradiction is structured: transfer among
the three modern generators averages 78.4%, whereas GPT-2 and the modern models
form almost disjoint detection domains.
Pooled leave-one-generator-out training detects unseen LFM, DeepSeek, and GLM
reviews with 91.1%, 95.1%, and 96.7% recall at approximately 2% human
false-positive rate. It detects only 0.1% of held-out GPT-2 reviews.
The human-only balanced label-permutation control produces 49.31% accuracy and
48.95% AUROC, consistent with chance.
Layout and provenance
<run_id>/adapter/: loadable PEFT adapter and tokenizer files.<run_id>/config.json: training configuration.<run_id>/metrics.json: validation/test metrics.runs.csv: complete adapter experiment ledger.cross_gen_results.json: structured transfer and LOGO matrices.adapter_manifest.json: SHA-256 hashes for datasets and every adapter file.PAPER_FINDINGS.md: paper-oriented interpretation and claim boundaries.- Dataset generation provenance:
exact prompts, sampling settings, raw/final hashes, providers, and explicitly
unrecorded details.
Historical configs created before dataset hashing was added may have no
data_sha256 field. The publication manifest preserves those configs unchanged
and records a SHA-256 of the frozen dataset path separately instead of rewriting
historical metadata.
Scope warning
These models are research detectors, not universal arbiters of authorship.
Performance can change under new generators, decoding settings, paraphrasing,
domains, and human populations. A positive prediction must not be treated as
proof that a person used AI.
