DAMASHA-MAS: Mixed-Authorship Adversarial Segmentation (Token Classification)

This repository contains a token-classification model trained on the DAMASHA-MAS benchmark, introduced in:

DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution

The model aims to segment mixed human–AI text at token level – i.e., decide for each token whether it was written by a human or an LLM, even under syntactic adversarial attacks.

• Base encoders:
  • FacebookAI/roberta-base
  • answerdotai/ModernBERT-base
• Architecture (high level): RoBERTa + ModernBERT feature fusion → BiGRU + CRF with the Info-Mask gating mechanism from the paper.
• Task: Token classification (binary authorship: human vs AI).
• Language: English
• License (this model): MIT
• Training data license: CC-BY-4.0 via the DAMASHA dataset.

If you use this model, please also cite the DAMASHA paper and dataset (see Citation section).

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1. Model Highlights

• Fine-grained mixed-authorship detection
  Predicts authorship per token, allowing reconstruction of human vs AI spans in long documents.

• Adversarially robust
  Trained and evaluated on syntactically attacked texts (misspelling, Unicode substitutions, invisible characters, punctuation swaps, case perturbations, and “all-mixed” attacks).

• Human-interpretable Info-Mask
  The architecture incorporates stylometric features (perplexity, POS density, punctuation density, lexical diversity, readability) via an Info-Mask module that gates token representations in an interpretable way.

• Strong reported performance (from the paper)
  On DAMASHA-MAS, the RMC* model (RoBERTa + ModernBERT + CRF + Info-Mask) achieves:

  • Token-level: Accuracy / Precision / Recall / F1 ≈ 0.98
  • Span-level (strict): SBDA ≈ 0.45, SegPre ≈ 0.41
  • Span-level (relaxed IoU ≥ 0.5): ≈ 0.82

⚠️ The exact numbers for this specific checkpoint may differ depending on training run and configuration. The values above are from the paper’s best configuration (RMC*).

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2. Intended Use

What this model is for

• Research on human–AI co-authorship

  • Studying where LLMs “take over” in mixed texts.
  • Analysing robustness of detectors under adversarial perturbations.

• Tooling / applications (with human oversight)

  • Assisting editors, educators, or moderators to highlight suspicious spans rather than making final decisions.
  • Exploring interpretability overlays (e.g., heatmaps over tokens) when combined with Info-Mask outputs.

What this model is not for

• Automated “cheating detector” / plagiarism court.
• High-stakes decisions affecting people’s livelihood, grades, or reputation without human review.
• Non-English or heavily code-mixed text (training data is English-centric).

Use this model as a signal, not a judge.

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3. Data: DAMASHA-MAS

The model is trained on the MAS benchmark released with the DAMASHA paper and hosted as the Hugging Face dataset:

• Dataset: saiteja33/DAMASHA

3.1 What’s in MAS?

MAS consists of mixed human–AI texts with explicit span tags:

• Human text comes from several corpora for domain diversity, including:

  • Reddit (M4-Reddit)
  • Yelp & /r/ChangeMyView (MAGE-YELP, MAGE-CMV)
  • News summaries (XSUM)
  • Wikipedia (M4-Wiki, MAGE-SQuAD)
  • ArXiv abstracts (MAGE-SciGen)
  • QA texts (MAGE-ELI5)

• AI text is generated by multiple modern LLMs:

  • DeepSeek-V3-671B (open-source)
  • GPT-4o, GPT-4.1, GPT-4.1-mini (closed-source)

3.2 Span tagging

Authorship is marked using explicit tags around AI spans:

• <AI_Start> … </AI_End> denote AI-generated segments within otherwise human text.
• The dataset stores text in a hybrid_text column, plus metadata such as has_pair, and adversarial variants include attack_name, tag_count, and attacked_text.
• Tags are sentence-level in annotation, but the model is trained to output token-level predictions for finer segmentation.

During training, these tags are converted into token labels (2 labels total; see config.id2label in the model files).

3.3 Adversarial attacks

MAS includes multiple syntactic attacks applied to the mixed text:

• Misspelling
• Unicode character substitution
• Invisible characters
• Punctuation substitution
• Upper/lower case swapping
• All-mixed combinations of the above

These perturbations make tokenization brittle and test robustness of detectors in realistic settings.

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4. Model Architecture & Training

4.1 Architecture (conceptual)

The model follows the Info-Mask RMC* architecture described in the DAMASHA paper:

1. Dual encoders
   • RoBERTa-base and ModernBERT-base encode the same input sequence.
2. Feature fusion
   • Hidden states from both encoders are fused into a shared representation.
3. Stylometric Info-Mask
   • Hand-crafted style features (perplexity, POS density, punctuation density, lexical diversity, readability) are projected, passed through multi-head attention, and turned into a scalar mask per token.
   • This mask gates the fused encoder states, down-weighting style-irrelevant tokens and emphasizing style-diagnostic ones. :contentReference[oaicite:16]{index=16}
4. Sequence model + CRF
   • A BiGRU layer captures sequential dependencies, followed by a CRF layer for structured token labeling with a sequence-level loss. :contentReference[oaicite:17]{index=17}

4.2 Training setup (from the paper)

Key hyperparameters used for the Info-Mask models on MAS:

• Number of labels: 2
• Max sequence length: 512
• Batch size: 64
• Epochs: 5
• Optimizer: AdamW (with cosine annealing LR schedule)
• Weight decay: 0.01
• Gradient clipping: 1.0
• Dropout: Dynamic 0.1–0.3 (initial 0.1)
• Warmup ratio: 0.1
• Early stopping patience: 2

Hardware & compute (as reported):

• AWS EC2 g6e.xlarge, NVIDIA L40S (48GB) GPU, Ubuntu 24.04
• ≈ 400 GPU hours for experiments.

The exact training script used for this checkpoint is available in the project GitHub:
https://github.com/saitejalekkala33/DAMASHA

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license: mit