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| """Label taxonomy and the coarse-grained collapses. | |
| Kept out of `detector.py` so it imports without torch — same reason | |
| `forensics.py` is its own module. `test_collapse.py` exercises it directly. | |
| DroidDetect predicts four classes. Two coarser views are more useful to a human | |
| moderator, and the Droid paper reports accuracy at each level (Large: ~97% | |
| weighted F1 binary, ~88.8% ternary — the full 4-way split is the hardest, | |
| because "adversarial" and "generated" are the same text with different styling). | |
| """ | |
| LABELS = [ | |
| "HUMAN_GENERATED", | |
| "MACHINE_GENERATED", | |
| "MACHINE_REFINED", | |
| "MACHINE_GENERATED_ADVERSARIAL", | |
| ] | |
| # "Who actually wrote this": the participant, a model, or both. | |
| # MACHINE_GENERATED_ADVERSARIAL folds into MACHINE — model output styled to look | |
| # hand-written is still model output. | |
| TERNARY_OF = { | |
| "HUMAN_GENERATED": "HUMAN", | |
| "MACHINE_GENERATED": "MACHINE", | |
| "MACHINE_GENERATED_ADVERSARIAL": "MACHINE", | |
| "MACHINE_REFINED": "REFINED", | |
| } | |
| TERNARY_LABELS = ["HUMAN", "MACHINE", "REFINED"] | |
| # The yes/no the old CodeT5+ Space returned. | |
| BINARY_OF = { | |
| "HUMAN_GENERATED": "HUMAN", | |
| "MACHINE_GENERATED": "MACHINE", | |
| "MACHINE_GENERATED_ADVERSARIAL": "MACHINE", | |
| "MACHINE_REFINED": "MACHINE", | |
| } | |
| BINARY_LABELS = ["HUMAN", "MACHINE"] | |
| TERNARY_MEANING = { | |
| "HUMAN": "Written by the participant", | |
| "MACHINE": "Model-written (including output styled to look hand-typed)", | |
| "REFINED": "Participant started it, a model completed or rewrote it", | |
| } | |
| def collapse(probs, mapping, order): | |
| """Sum a 4-class distribution into a coarser one. | |
| Returns (coarse_probs, top_label, top_confidence). | |
| Summing is the correct collapse because the four classes are mutually | |
| exclusive outcomes of one softmax. It also fixes a real failure of the raw | |
| argmax: when a model's output is genuinely ambiguous between "generated" | |
| and "adversarial", each class can sit below a human plurality while their | |
| sum is decisively machine. e.g. 0.40 human vs 0.31 + 0.27 machine — the | |
| 4-way argmax says HUMAN, the ternary correctly says MACHINE at 0.58. | |
| """ | |
| out = {k: 0.0 for k in order} | |
| for label, p in probs.items(): | |
| out[mapping[label]] += p | |
| out = {k: round(v, 6) for k, v in out.items()} | |
| top = max(out, key=out.get) | |
| return out, top, out[top] | |