Whisper Hallucination Detection and Mitigation via Hidden Representation Steering and Sparse AutoEncoders
arXiv:2606.07473v1 Announce Type: cross Abstract: Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconnected from the input. We investigate whether hallucinations can be detected and mitigated through Whisper's internal representations. We extract audio encoder activations and evaluate two representation spaces: raw Whisper activations and Sparse AutoEncoder (SAE) latents. We show that both spaces encode linearly separable hallucination-related information, with discriminative power concentrated in
Ongoing advancements in AI models, specifically ASR systems like Whisper, are reaching a point where understanding and mitigating inherent failure modes such as hallucination is critical for broader adoption and reliability.
Improving the reliability and trustworthiness of ASR systems by detecting and mitigating hallucinations is crucial for sensitive applications and for enhancing user confidence in AI-generated content across various sectors.
The ability to internally detect and mitigate hallucinations in ASR models like Whisper reduces the risk of generating coherent but incorrect transcriptions, making these systems more robust and deployable in high-stakes environments.
- · AI developers
- · Speech-to-text service providers
- · Compliance and legal tech
- · Accessibility technology
- · Providers of unreliable ASR systems
- · AI applications heavily dependent on perfect transcription
Increased trustworthiness and deployment of ASR technology in critical communication and data entry tasks.
Reduced need for extensive human oversight in transcription, leading to efficiency gains in various industries.
Enhanced overall reliability of AI agents and automated systems that rely on accurate speech input for decision-making.
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Read at arXiv cs.AI