6 papers
TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio
Chaewan Chun, Meruyert Aristombayeva, Jiyoung Choi +3
Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly on clean written claims. Spoken dialogue remains different even when sy…
Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts
Meruyert Aristombayeva, Jason S. Lucas, Chaewan Chun +1
While text-based hallucination detection is well studied, reference-free detection of factual alterations in speech remains underexplored, especially for low-resource languages. Ou…
Context-Aware Multimodal Claim Verification in Spoken Dialogues
Chaewan Chun, Delvin Ce Zhang, Dongwon Lee
Spoken factual claims often occur within multi-turn conversations, where surrounding dialogue can provide context unavailable from the claim alone. Yet most fact-checking research…
AOR-Bench: Do Large Audio Language Models Over-Refuse Pseudo-Harmful Queries?
Jiaxi Yang, Chaewan Chun, Jason Lucas +2
Large Audio Language Models (LALMs) have demonstrated strong performance across a wide range of audio tasks. As they are increasingly deployed in real-world applications, ensuring…
When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms
Chaewan Chun, Delvin Ce Zhang, Dongwon Lee
Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and live streams. With millions of…
MAD: A Benchmark for Multi-Turn Audio Dialogue Fact-Checking
Chaewan Chun, Lysandre Terrisse, Delvin Ce Zhang +1
Despite the growing popularity of audio platforms, fact-checking spoken content remains significantly underdeveloped. Misinformation in speech often unfolds across multi-turn dialo…