构建音频推理与检索结合的新基准，挑战大模型真实场景理解能力
AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
- 设计真实网络环境下的音频问答任务，融合外部信息检索
- 顶尖音频大模型在新基准上表现不佳，准确率低于40%
- 提出智能体式流程，实现音频推理与检索增强生成的协同
随着大型音频-语言模型（LALMs）在声音、语音和音乐相关任务中展现出卓越性能，越来越多的研究开始关注评估这些模型的基准。现有基准多聚焦于基于内部知识的推理，忽视了需要外部信息支撑的真实应用场景。为弥补这一空白，我们提出了AudioRAG——一个面向真实网络环境下音频推理与信息检索结合的新型基准。该基准包含自动生成和人工标注的问答对。评估结果显示，即使是当前最先进的LALMs也难以有效回答这些问题。因此，我们提出一种智能体式流水线，将音频推理与检索增强生成相结合，为未来研究提供更强的基线。
原文摘要 · Abstract (English)
Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a growing interest in proposing benchmarks to assess these models. Existing benchmarks generally focus only on reasoning with internal knowledge, neglecting real-world scenarios that require external information grounding. To bridge this gap, we introduce AudioRAG, a novel benchmark designed to evaluate audio-based reasoning augmented by information retrieval in realistic web environments. This benchmark comprises both LLM-generated and manually curated question-answer pairs. Our evaluations reveal that even the state-of-the-art LALMs struggle to answer these questions. We therefore propose an agentic pipeline that integrates audio reasoning with retrieval-augmented generation, providing a stronger baseline for future research.
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