FRAUDSkill: Structured Frozen-Weight Skill Optimization for Audio Anti-Fraud Detection
FRAUDSkill adapts frozen audio-language models for telecom fraud detection via external skill programs, reaching 73.50% Macro-F1.
FRAUDSkill is a structured frozen-weight adaptation framework for audio anti-fraud detection that optimizes external skill programs, route-specific policies, and decision rules without modifying the underlying audio-language model. On the TeleAntiFraud benchmark it achieves 73.50% Macro-F1, a 31.96% improvement over the shared frozen-model baseline, while reducing invalid outputs to 1.94%. Source code is publicly available.
- External skill optimization leaves the base audio-language model unchanged
- 73.50% Macro-F1 on TeleAntiFraud, +31.96% over frozen baseline
- Reduces invalid outputs to 1.94% via structured output control
- Uses validation-guided multi-path inference for protocol compliance
From the original article
1 min read at huggingface.co
Large audio-language models have shown promise for anti-fraud detection by directly processing speech and reasoning over fraud-related evidence. Their deployment, however, requires predictions to follow a predefined label space and a structured decision protocol consisting of service-scenario identification, fraud detection, and conditional fraud-type classification.
By Chengxian Hu, Zhiming Ma, Mingjun Pan · huggingface.co
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