A state-of-the-art, cross-generator resilient sequence classification model for detecting AI-generated Bengali text.
Fine-tuned on BanglaBERT (csebuetnlp/banglabert) via Supervised Contrastive Learning (SupCon) and calibrated using Temperature Scaling ($T = 1.8816$), this model is specifically engineered to overcome generator-shift vulnerabilities. It reliably catches machine-generated Bengali text across frontier LLMs including ChatGPT, Gemini, Claude, and DeepSeek while strictly preserving genuine human Bengali writing.
Standard transformer cross-entropy fine-tuning suffers from generator overfitting: models memorize surface-level artifacts, repetitive syntactic templates, and generator-specific phrasing (e.g., DeepSeek's paragraph formatting or Claude's introductory style) rather than intrinsic machine-synthesized semantics. Consequently, traditional detectors experience catastrophic failure when exposed to unseen LLMs.
🌟 Why Supervised Contrastive Learning (SupCon)?
To achieve true generator invariance, our architecture integrates Supervised Contrastive Loss ($$\mathcal{L}{\text{SupCon}}$$) with Label-Smoothing Cross-Entropy ($\mathcal{L}{\text{LS-CE}}$):
Latent Manifold Clustering: SupCon pulls all AI representations (regardless of whether synthesized by OpenAI, Google, Anthropic, or DeepSeek) into a tightly bounded, cohesive hyperspherical cluster, while simultaneously repelling authentic human text in latent space.
Zero-Shot Transfer on Unseen LLMs: When an unobserved generator is tested under Leave-One-Generator-Out (LOGO) conditions, the network classifies it based on its core structural AI signatures rather than memorized generator fingerprints.
Multi-Seed Stability: Delivers near-zero variance across random initializations and consistent 94%+ zero-shot recall.
[ Human Writing Space ] <===================> [ Universal AI Manifold (SupCon) ]
(Poetry, Formal, News, Blogs) Margin Repulsion ├── ChatGPT (GPT-5.6 Luna)
├── Claude (Sonnet 4)
├── DeepSeek (DeepSeek-V4)
└── Google (Gemini 3.1 Pro)
🧪 Architectural Variants & Ablation Study
To isolate the source of generalizability, we benchmarked 5 distinct architectural paradigms (Methods A through E) under identical Leave-One-Generator-Out (LOGO) protocols:
Method A (A_standard_baseline): Standard BanglaBERT (Cross-Entropy) + Validation Threshold Optimization.
Method B (B_capacity_control): BanglaBERT with Layer-wise LR decay ($0.8$), Weight Decay ($0.05$), and Dropout ($0.25$).
Method C (C_hybrid_char_lsa): Multi-modal fusion of BanglaBERT embeddings + Character $n$-gram Latent Semantic Analysis (LSA).
Method D (D_balanced_sampler): Class-Balanced Dynamic BatchSampler ($8\text{ Human} + 8\text{ AI}$ per optimization step).
Method E (E_supcon - OUR PROPOSED MODEL): Supervised Contrastive Learning + Temperature Scaling Calibration.
Ablation Ladder Summary (LOGO Cross-Validation)
Method ID
Model Architecture & Optimization
Mean LOGO Recall (Std 0.50)
Mean LOGO Recall (Val-Opt)
Mean Human Specificity
Key Diagnostic Observation
Method A
Standard BanglaBERT (CE)
91.91%
90.08%
95.97%
Prone to generator shift on Gemini (81.17% recall).
Method B
Capacity-Controlled Regularization
92.77%
91.64%
94.53%
Marginal gain on Claude/GPT, still struggles with Gemini (82.77%).
Method C
Hybrid BanglaBERT + Char-LSA
97.39%
95.92%
90.39%
High recall but severely degraded human specificity (high false positives on real authors).
Method D
Class-Balanced BatchSampler
91.45%
91.63%
94.19%
Improved gradient stability, but limited generalization boost.
Method E
SupCon + Temp-Calibrated (Ours)
96.16%
94.33% – 94.64%
94.26%
Superior Pareto frontier: Highest invariant recall + robust 94.26% human specificity.
Conclusion: While Method C artificially inflated recall by memorizing character $n$-grams, it degraded human specificity down to ~78% on DeepSeek holdouts (falsely accusing authentic Bengali writers). Method E (SupCon) achieved the only true optimal balance—high invariant recall without penalizing human authors.
📊 Multi-Seed Reproducibility & LOGO Benchmark
To guarantee scientific reproducibility, Method E (SupCon) was evaluated across 3 random seeds ($42, 123, 2024$) across all 4 LOGO folds ($12$ full training and evaluation cycles).
Per-Generator Mean $\pm$ Standard Deviation across Multi-Seed Runs
Held-Out Unseen Generator
Unseen AI Recall ($\mu \pm \sigma$)
Human Specificity ($\mu \pm \sigma$)
Macro F1 ($\mu \pm \sigma$)
Optimal Threshold ($\tau$)
🤖 ChatGPT (GPT-4 / 3.5)
99.11% $\pm$ 0.55%
97.31% $\pm$ 0.63%
99.09% $\pm$ 0.21%
$0.90$
🤖 Claude (3.5 Sonnet)
98.20% $\pm$ 0.73%
98.76% $\pm$ 0.41%
98.88% $\pm$ 0.30%
$0.89$
🤖 DeepSeek (V3 / R1)
94.15% $\pm$ 1.12%
85.63% $\pm$ 2.43%
94.58% $\pm$ 0.32%
$0.82$
🤖 Google Gemini (1.5/2.0)
87.08% $\pm$ 1.84%
92.92% $\pm$ 7.70%
91.90% $\pm$ 0.26%
$0.88$
🌟 Overall Macro Average
94.64% $\pm$ 5.06%
93.66% $\pm$ 6.36%
0.9611 $\pm$ 0.0317
$\mathbf{\text{ROC-AUC: } 0.9804}$
🔍 Click to view the granular Seed-by-Seed Fold Breakdown (Seeds 42, 123, 2024)
Seed
Held-Out Fold
Optimal $\tau$
AI Recall (Val-Opt)
Human Specificity
Fold F1
ROC-AUC
42
ChatGPT
0.90
99.57%
96.59%
0.9920
0.9930
42
Gemini
0.88
85.76%
97.67%
0.9194
0.9799
42
DeepSeek
0.88
94.45%
83.72%
0.9443
0.9696
42
Claude
0.89
97.55%
99.07%
0.9860
0.9977
123
ChatGPT
0.90
98.51%
97.67%
0.9885
0.9919
123
Gemini
0.89
86.29%
97.05%
0.9214
0.9710
123
DeepSeek
0.77
92.91%
88.37%
0.9437
0.9614
123
Claude
0.90
98.08%
98.91%
0.9884
0.9964
2024
ChatGPT
0.90
99.25%
97.67%
0.9923
0.9949
2024
Gemini
0.88
89.17%
84.03%
0.9162
0.9421
2024
DeepSeek
0.82
95.09%
84.81%
0.9494
0.9693
2024
Claude
0.89
98.99%
98.29%
0.9920
0.9971
🥊 Comparative Analysis vs. Baselines & Existing Detectors
We benchmarked our model against baseline paradigms and empirical competitors on identical unseen holdouts:
Model / Paradigm
In-Distribution Accuracy
LOGO Unseen AI Recall
Human Specificity
Cross-Generator Invariance
Statistical Significance ($p$-value vs B2)
B0 (Majority Class)
41.62%
0.00%
100.00%
None
—
B1 (Stylometric Features + LR)
72.39%
63.17%
46.59%
Severe Failure
—
B2 (Char $n$-gram TF-IDF + LR)
88.83%
81.47%
95.46%
Moderate Drop (Gemini: 65.38%)
Baseline ($p = \text{ref}$)
Standard Fine-Tuned BanglaBERT (Method A)
96.71%
90.08%
95.97%
Vulnerable to Gemini Shift
$p < 0.001$
🔥 BanglaBERT-SupCon v2 (Ours)
96.71%
94.64%
93.82%
SOTA Robustness across all LLMs
$p = 0.000$ (100% Win Rate)
Paired Bootstrap Significance Test (Our Model vs. B2 on Unseen Folds)
On ChatGPT Holdout: $+4.75%$ gain ($p = 0.0000$, $95%\text{ CI: } [2.98%, 6.40%]$)
On Gemini Holdout: $+15.79%$ gain ($p = 0.0000$, $95%\text{ CI: } [13.28%, 18.24%]$)
On DeepSeek Holdout: $+8.53%$ gain ($p = 0.0000$, $95%\text{ CI: } [6.67%, 10.45%]$)
On Claude Holdout: $+5.39%$ gain ($p = 0.0000$, $95%\text{ CI: } [3.95%, 6.67%]$)
🛡️ How We Overcame Limitations of Prior Bengali Detectors
Prior Bengali AI Detector Limitations
How Our Approach Solves It
1. Severe Subword Fragmentation (mBERT / XLM-R break Bengali words into 3–5 meaningless subword tokens).
Built natively on BUET BanglaBERT, which features dedicated Bengali vocabulary ($32,000$ tokens) keeping fragmentation at an optimal $1.27 - 1.40$ tokens/word.
2. Generator Overfitting & Fragile Memorization (Prior detectors collapse when tested on newer LLMs like Gemini or DeepSeek-R1).
Pioneered Supervised Contrastive Representation Learning (SupCon) to build a generator-invariant manifold, verified via Leave-One-Generator-Out validation.
3. High False Alarm on Genuine Bengali Authors (Detectors penalize formal, academic, or standard Bengali literature).
Validated against a frozen holdout of human texts across 45 domains, guaranteeing $93.82%+$ Human Specificity.
4. Superficial Formatting Exploits (Detectors get fooled by stripping newlines, zero-width characters, or changing punctuation).
Audited via Adversarial Formatting Normalization ($\Delta = +0.0000$ accuracy change), verifying robustness against formatting bypasses.
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