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To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech

Paper recorded by Signals 4 on 2026-09-24 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.AI · 人工智能 · first seen 2026-09-25

Abstract

Online 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). VeriSpeak contains 3,879 spoken claims

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#6 most recent of 400 cs.AI papers we have recorded · ↑ newer: Coding Agents for Generalized Task and Motion Planning Problems · ↓ older: PoEM: Predicting RL Outcomes from Existing Policies
Cite this page: To Trust or Not to Trust: Retrieval-Augmented Fact Checking in Speech: the #6 most recent of 400 cs.AI papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/to-trust-or-not-to-trust-retrieval-augmented-fact-checking-in-speech.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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