LLMs show mixed results on Massive Sound Embedding Benchmark

A new paper evaluates leading Large Language Models, including those from the Gemini and GPT families, on the Massive Sound Embedding Benchmark (MSEB). The study assesses their capabilities across eight core audio tasks to determine their effectiveness and audio-text parity. While a notable gap in performance and robustness between specialized audio models and these LLMs persists, the research suggests that the optimal architecture remains unclear, depending on specific application needs. AI

影响 Evaluates the current state of LLMs in audio processing, highlighting a persistent gap and the need for task-specific architectural choices.

排序理由 Academic paper evaluating existing LLMs on a specific benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• Gemini
• GPT
• Massive Sound Embedding Benchmark

• 论文
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