VCB Bench：用于音频基础大语言模型对话代理的评估基准
VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
AI总结：
针对现有音频语言模型基准局限，提出VCB Bench，基于真实人类语音构建，从指令跟随、知识理解、鲁棒性三个角度评估，揭示性能差距并指明改进方向，提供评估框架。
摘要：
大型音频语言模型的进展增强了多模态对话系统，但现有基准有局限。为此提出VCB Bench，这是一个完全基于真实人类语音构建的高质量中文基准。它从三个互补角度评估大型音频语言模型，实验揭示了性能差距并突出了改进方向，提供了可重复且细粒度的评估框架。
英文摘要：
Recent advances in large audio language models (LALMs) have greatly enhanced multimodal conversational systems. However, existing benchmarks remain limited -- they are mainly English-centric, rely on synthetic speech, and lack comprehensive, discriminative evaluation across multiple dimensions. To address these gaps, we present Voice Chat Bot Bench (VCB Bench) -- a high-quality Chinese benchmark built entirely on real human speech. VCB Bench evaluates LALMs from three complementary perspectives: instruction following (including speech-level control beyond text commands), knowledge understanding (general knowledge, reasoning, and daily dialogue), and robustness (stability under perturbations in content, environment, and speaker traits). Experiments on representative LALMs reveal notable performance gaps and highlight future directions for improvement. VCB Bench provides a reproducible and fine-grained evaluation framework, offering standardized methodology and practical insights for advancing Chinese voice conversational models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
