title: TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud
    Detection
summary: 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.
paper_authors._id: 67eb5116d3a707c0a5b02bd1
paper_authors.name: Zhiming Ma
paper_authors.status: extracted_pending
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paper_authors.fullname: ZhimingMa
paper_authors.type: user
paper_authors.user: JimmyMa99
paper_authors._id: 67eb5116d3a707c0a5b02bd2
paper_authors.name: Peidong Wang
paper_authors.status: extracted_pending
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paper_authors.fullname: Peidong Wang
paper_authors.type: user
paper_authors.user: WDong
paper_authors._id: 67eb5116d3a707c0a5b02bd3
paper_authors.name: Minhua Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-01T08:02:15.224Z
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paper_authors.avatarUrl: /avatars/9659b7d0f6fa51efc127afb7a1ba14b1.svg
paper_authors.fullname: HuangMinhua
paper_authors.type: user
paper_authors.user: HuangMinhua
paper_authors._id: 67eb5116d3a707c0a5b02bd4
paper_authors.name: Jingpeng Wang
paper_authors._id: 67eb5116d3a707c0a5b02bd5
paper_authors.name: Kai Wu
paper_authors._id: 67eb5116d3a707c0a5b02bd6
paper_authors.name: Xiangzhao Lv
paper_authors._id: 67eb5116d3a707c0a5b02bd7
paper_authors.name: Yachun Pang
paper_authors._id: 67eb5116d3a707c0a5b02bd8
paper_authors.name: Yin Yang
paper_authors._id: 67eb5116d3a707c0a5b02bd9
paper_authors.name: Wenjie Tang
paper_authors._id: 67eb5116d3a707c0a5b02bda
paper_authors.name: Yuchen Kang
paper_title: TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud
    Detection
paper_summary: 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.
paper_ai_summary: TeleAntiFraud-28k is an open-source dataset combining audio and text for telecom fraud detection, featuring privacy-preserving text generation and adversarial synthesis.
paper_ai_keywords: automatically speech recognition ASR text-to-speech TTS large language model LLM multi-agent adversarial synthesis scenario classification fraud detection supervised fine-tuning SFT