It is found that fine-tuning is a prerequisite for these inference-time techniques: without domain adaptation, prompting and shallow fusion cause severe hallucination on air traffic control speech, suggesting that domain difficulty determines whether prompting can work without fine-tuning.
Candor-LR is introduced, a conversational benchmark derived from the CANDOR corpus of 1,656 natural dyadic videoconferences that significantly improves cross-domain robustness under both clean and noisy conditions, as its realistic conversational data captures broader audio-video features.
Rishabh Jain, A. Papadopoulos, Zhao-Feng Lin et al.· 0 citations
Deepfake voice detection suffers from poor generalization across unseen domains. While Audio Large Language Models (ALLMs) show promise, the modality gap between continuous audio embeddings which capture the subtle acoustic details necessary for deepfake detection and the semantic space of LLMs remains a critical, unde...
Y. El Kheir, Xin Wang, Wan-Ying Ge et al.· 1 citation
This work presents AudioChaps, a post-training framework for aligning end-to-end LALMs for this task via Group Relative Policy Optimization (GRPO) guided by Chain-of-Thought (CoT) reasoning, and demonstrates that GRPO-trained LALMs can reliably transform unstructured auditory streams into navigable, structured media.
Tony Alex, Wish Suharitdamrong, Sara Atito et al.· 0 citations
It is found that album review supervision yields the largest retrieval gains on a human-written caption benchmark (Song Describer), particularly for complex queries that other existing caption datasets leave uncovered.
Pablo Alonso-Jiménez, Xavier Lizarraga-Seijas, Xavier Serra et al.· 0 citations
Multimodal large language models (MLLMs) are rapidly evolving toward continuous audio--visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio--visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption scores provide covera...
Zhongyu Yang, Jia-Le Tao, Rui-Tao Chen et al.· 0 citations
VideoVista is presented, a versatile multilingual and multidomain video comprehension benchmark, built via scalable, quality-controlled automatic QA generation and manual refinement, and released at https://github.com/HITsz-TMG/VideoVista.
Yunxin Li, Xin-Yu Chen, Zhenran Xu et al.· IEEE Transactions on Pattern...· 0 citations
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