S2SBench: A Benchmark for Quantifying Intelligence Degradation in Speech-to-Speech Large Language Models

• URL: http://arxiv.org/abs/2505.14438v1
• Date: Tue, 20 May 2025 14:42:20 GMT
• Title: S2SBench: A Benchmark for Quantifying Intelligence Degradation in Speech-to-Speech Large Language Models
• Authors: Yuanbo Fang, Haoze Sun, Jun Liu, Tao Zhang, Zenan Zhou, Weipeng Chen, Xiaofen Xing, Xiangmin Xu,
• Abstract summary: End-to-end speech large language models ((LLMs)) extend the capabilities of text-based models to directly process and generate audio tokens.<n>This often leads to a decline in reasoning and generation performance compared to text input.<n>We propose S2SBench, a benchmark designed to quantify performance degradation in Speech LLMs.
• Score: 14.060679420379516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-end speech large language models ((LLMs)) extend the capabilities of text-based models to directly process and generate audio tokens. However, this often leads to a decline in reasoning and generation performance compared to text input, a phenomenon referred to as intelligence degradation. To systematically evaluate this gap, we propose S2SBench, a benchmark designed to quantify performance degradation in Speech LLMs. It includes diagnostic datasets targeting sentence continuation and commonsense reasoning under audio input. We further introduce a pairwise evaluation protocol based on perplexity differences between plausible and implausible samples to measure degradation relative to text input. We apply S2SBench to analyze the training process of Baichuan-Audio, which further demonstrates the benchmark's effectiveness. All datasets and evaluation code are available at https://github.com/undobug/S2SBench.

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