Model Card: AI-in-the-Loop for Real-Time Scam Detection & Scam-Baiting

This repository contains instruction-tuned large language models (LLMs) designed for real-time scam detection, conversational scam-baiting, and privacy-preserving federated learning.
The models are trained and evaluated as part of the paper:
AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning

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Model Details

• Developed by: Supreme Lab, University of Texas at El Paso & Southern Illinois University Carbondale
• Funded by: U.S. National Science Foundation (Award No. 2451946) and U.S. Nuclear Regulatory Commission (Award No. 31310025M0012)
• Shared by: Ismail Hossain, Sai Puppala, Sajedul Talukder, Md Jahangir Alam
• Model type: Multi-task instruction-tuned LLMs (classification + safe text generation)
• Languages: English
• License: MIT
• Finetuned from: LlamaGuard family & MD-Judge

Model Sources

• Repository: GitHub – supreme-lab/ai-in-the-loop
• Hugging Face: supreme-lab/ai-in-the-loop
• Paper: ArXiv version

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Uses

Direct Use

• Real-time scam classification (scam vs. non-scam conversations)
• Conversational scam-baiting to waste scammer time safely
• PII risk scoring to filter unsafe outputs

Downstream Use

• Integration into messaging platforms for scam prevention
• Benchmarks for AI safety alignment in adversarial contexts
• Research in federated privacy-preserving LLMs

Out-of-Scope Use

• Should not be used as a replacement for law enforcement tools
• Should not be deployed without safety filters and human-in-the-loop monitoring
• Not intended for financial or medical decision-making

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Bias, Risks, and Limitations

• Models may over-engage with scammers in rare cases
• Possible false positives in benign conversations
• Cultural/linguistic bias: trained primarily on English data
• Risk of hallucination when generating long responses

Recommendations

• Always deploy with safety thresholds (δ, θ1, θ2)
• Use in controlled environments first (research, simulations)
• Extend to multilingual settings before real-world deployment

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How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer
# Replase the <x> with 2 or 3 and Nothing (when it is llama-guard-multi-task)
model_id = "supreme-lab/ai-in-the-loop/llama-guard-<x>-multi-task"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Scammer: Hello, I need your SSN.", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

• Classification: SSD, SSC, SASC, MASC (synthetic scam/non-scam dialogues)
• Generation: SBC (254 real scam-baiting convs), ASB (>37k msgs), YTSC (YouTube scam transcriptions)
• Auxiliary: ConvAI, DailyDialog (engagement), HarmfulQA, Microsoft PII dataset

Training Procedure

• Fine-tuning setup:
  • 3 epochs, batch size = 8
  • LoRA rank = 8, α = 16
  • Mixed precision (bf16)
  • Optimizer: AdamW
• Federated Learning (FL):
  • Simulated 10 clients, 30 rounds FedAvg
  • Optional Differential Privacy (noise multipliers: 0.1, 0.8)

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Evaluation

Metrics

• Classification: F1, AUPRC, FPR, FNR
• Generation: Perplexity, Distinct-1/2, DialogRPT, BERTScore, ROUGE-L, HarmBench

Results

• Classification: BiGRU/BiLSTM > 0.99 F1, RoBERTa competitive
• Instruction-tuned LLMs: MD-Judge best overall (F1 = 0.89+), LlamaGuard3 strong for moderation
• Generation: MD-Judge achieved lowest perplexity (22.3), highest engagement (0.79), 96% safety compliance in human evals

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Environmental Impact

• Hardware: NVIDIA H100 GPUs
• Training Time: ~30 hrs across models
• Federated Setup: 10 simulated clients, 30 rounds

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Technical Specifications

• Architecture: Instruction-tuned transformer (decoder-only)
• Objective: Multi-task (classification, risk scoring, safe generation)

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Citation

If you use these models, please cite our paper:

@article{hossain2025aiintheloop,
  title={AI-in-the-Loop: Privacy Preserving Real-Time Scam Detection and Conversational Scambaiting by Leveraging LLMs and Federated Learning},
  author={Hossain, Ismail; Puppala, Sai; Alam, Md Jahangir; and Talukder, Sajedul},
  journal={[arXiv preprint arXiv:2509.05362](https://arxiv.org/abs/2509.05362)},
  year={2025}
}

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Contact

• Authors: ihossain@miners.utep.edu, sai.puppala@siu.edu, stalukder@utep.edu
• Lab: Supreme Lab
• Personal Web: https://ismail102.github.io/

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