Benchmarking LLMs on the Massive Sound Embedding Benchmark (MSEB)
The Massive Sound Embedding Benchmark (MSEB) has emerged as a standard for evaluating the functional breadth of audio models. While initial baselines focused on specialized encoders, the shift toward "audio-native" Large Language Models (LLMs) suggests a new paradigm where a single multimodal backbone may replace complex, task-specific pipelines. This paper provides a rigorous empirical evaluation of leading LLMs - including members from the Gemini and GPT families - across the eight core MSEB capabilities to assess their efficacy and audio-text parity. Our results indicate that while a significant modality gap persists regarding performance and robustness, the empirical evidence for an "...
| Authors | Cyril Allauzen, Tom Bagby, Georg Heigold et al. |
|---|---|
| Primary category | cs.SD |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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