XModBench is a tri-modal benchmark that systematically measures cross-modal consistency, modality disparities, and directional imbalances in omni-language models across five task families and all modality combinations.
Audiobench: A universal benchmark for audio large language models
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VoiceBench is the first benchmark for multi-faceted evaluation of LLM voice assistants using real and synthetic spoken instructions with speaker, environmental, and content variations.
Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.
A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.
citing papers explorer
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XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models
XModBench is a tri-modal benchmark that systematically measures cross-modal consistency, modality disparities, and directional imbalances in omni-language models across five task families and all modality combinations.
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VoiceBench: Benchmarking LLM-Based Voice Assistants
VoiceBench is the first benchmark for multi-faceted evaluation of LLM voice assistants using real and synthetic spoken instructions with speaker, environmental, and content variations.
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All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation
Audio-language models retain 60-72% of benchmark scores without audio, and most audio-dependent items can be solved from short fragments rather than full clips.
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On The Landscape of Spoken Language Models: A Comprehensive Survey
A literature survey that organizes spoken language models by architecture, training, and evaluation choices and identifies key challenges and future directions.