# TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection

arXiv: https://arxiv.org/abs/2503.24115 · HTML: https://mlsanity.com/paper/2503.24115/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot](https://mlsanity.com/paper/2412.02612/) (2024)
- [Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems](https://mlsanity.com/paper/2412.09413/) (2024)
- [FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs](https://mlsanity.com/paper/2407.04051/) (2024)
- [Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction](https://mlsanity.com/paper/2502.11946/) (2025)
- [LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language](https://mlsanity.com/paper/2402.16929/) (2024)
- ["It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams](https://mlsanity.com/paper/2502.03964/) (2025)
- [Detecting Scams Using Large Language Models](https://mlsanity.com/paper/2402.03147/) (2024)
- [Combating Phone Scams with LLM-based Detection: Where Do We Stand?](https://mlsanity.com/paper/2409.11643/) (2024)
- [Advanced Real-Time Fraud Detection Using RAG-Based LLMs](https://mlsanity.com/paper/2501.15290/) (2025)
- [ICASSP 2021 Deep Noise Suppression Challenge: Decoupling Magnitude and Phase Optimization with a Two-Stage Deep Network](https://mlsanity.com/paper/2102.04198/) (2021)
- [Exposing LLM Vulnerabilities: Adversarial Scam Detection and Performance](https://mlsanity.com/paper/2412.00621/) (2024)

## Built on by (most cited first, 12 of 12)

- [Open-Jev Judgments on CallScreenBench: Calibrated One-Pass Scam Screening with a Small Language Model](https://mlsanity.com/paper/2609.23959/) (2026)
- [SAFE-QAQ: End-to-End Slow-Thinking Audio-Text Fraud Detection via Reinforcement Learning](https://mlsanity.com/paper/2601.01392/) (2026)
- [PreScam: A Benchmark for Predicting Scam Progression from Early Conversations](https://mlsanity.com/paper/2605.12243/) (2026)
- [Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate](https://mlsanity.com/paper/2608.24127/) (2026)
- [Vishing-Tactics-Bench: Forecasting Exploitation Trajectories in Voice Phishing Calls](https://mlsanity.com/paper/2609.07151/) (2026)
- [ORACLE: Anticipating Scams from Partial Trajectories in Streaming App Usage](https://mlsanity.com/paper/2605.16363/) (2026)
- [FraudSMSWalker: Benchmarking Agentic Large Language Models for SMS-to-Webpage Fraud Detection](https://mlsanity.com/paper/2606.16659/) (2026)
- [TeleAntiFraud 2.0: A Refreshable, Profile-Grounded, and Audio-Based Benchmark for Telecom Fraud Detection](https://mlsanity.com/paper/2609.18748/) (2026)
- [FRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection](https://mlsanity.com/paper/2609.18766/) (2026)
- [Before the Warning Comes Too Late: Incremental Phone-Scam Detection from Speech](https://mlsanity.com/paper/2609.20223/) (2026)
- [A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot](https://mlsanity.com/paper/2609.29528/) (2026)
- [Evi-VN: Hard Region Guided Virtual Node Evidence Injection for GNN-Based Fraud Detection](https://mlsanity.com/paper/2610.11665/) (2026)

## Abstract (arXiv)

The detection of telecom fraud faces significant challenges due to the lack of high-quality multimodal training data that integrates audio signals with reasoning-oriented textual analysis. To address this gap, we present TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis. Our dataset is constructed through three strategies: (1) Privacy-preserved text-truth sample generation using automatically speech recognition (ASR)-transcribed call recordings (with anonymized original audio), ensuring real-world consistency through text-to-speech (TTS) model regeneration; (2) Semantic enhancement via large language model (LLM)-based self-instruction sampling on authentic ASR outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis that simulates emerging fraud tactics through predefined communication scenarios and fraud typologies. The generated dataset contains 28,511 rigorously processed speech-text pairs, complete with detailed annotations for fraud reasoning. The dataset is divided into three tasks: scenario classification, fraud detection, fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a standardized evaluation benchmark comprising proportionally sampled instances from the dataset, to facilitate systematic testing of model performance on telecom fraud detection tasks. We also contribute a production-optimized supervised fine-tuning (SFT) model trained on hybrid real/synthetic data, while open-sourcing the data processing framework to enable community-driven dataset expansion. This work establishes a foundational framework for multimodal anti-fraud research while addressing critical challenges in data privacy and scenario diversity. The project will be released at https://github.com/JimmyMa99/TeleAntiFraud.