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This paper benchmarks the performance of state-of-the-art LLMs, including Gemini and GPT models, on the Massive Sound Embedding Benchmark (MSEB) across eight core audio capabilities. The study reveals a significant modality gap between audio and text processing in LLMs, indicating that audio-native LLMs do not yet consistently outperform cascaded architectures. The authors conclude that the optimal modeling approach depends on specific use-case requirements, considering factors like latency, cost, and reasoning depth.
Audio-native LLMs still lag behind cascaded architectures in key audio tasks, suggesting that the multimodal promise of LLMs isn't quite ready for prime time in the sound domain.
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"optimal"modeling approach remains inconclusive. Ultimately, the choice between audionative and cascaded architectures depends heavily on specific use-case requirements and the underlying assumptions regarding latency, cost, and reasoning depth.
