To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech
VeriSpeak tests whether audio language models can verify 3,879 spoken claims, with retrieval plus reasoning reaching 86.1%.
VeriSpeak is a public probe benchmark of 3,879 spoken claims spanning temporal, geographical, and relational facts, with balanced true and false labels, for fact-checking by large audio language models. Experiments show a consistent text-to-speech gap: models that verify written claims reliably often fail on the same claims when spoken. Retrieval alone adds limited benefit because models conflate retrieved evidence with the spoken claim, while retrieval plus explicit reasoning improves comparison, and a thinking-tuned model reached 86.1% accuracy. The dataset is released on Hugging Face.
- VeriSpeak contains 3,879 spoken claims with balanced true and false labels.
- Claims cover temporal, geographical, and relational facts from spoken media.
- Models that verify written claims often fail the same claims when spoken.
- Retrieval alone helps little because models conflate evidence with the claim.
- Retrieval plus explicit reasoning reached 86.1% on a thinking-tuned audio model.
- Products
- VeriSpeak
- Organizations
- Hugging Face
From the original article
1 min read at arxiv.orgOnline misinformation increasingly appears in spoken formats such as news clips, podcasts, interviews, political speeches, and social media videos, creating a need for fact-checking systems that can verify claims directly from speech. We introduce VeriSpeak, a probe benchmark for studying speech-based fact verification in Large Audio Language Models (LALMs).
By Debajyoti Mazumder · arxiv.org
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