AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
Methodology
The methodology is well-structured, introducing AudioRAG as a benchmark that combines audio reasoning with information retrieval. The authors employ both LLM-generated and manually curated questions, which is a thoughtful approach to ensure diversity and relevance in the dataset. The use of an agentic pipeline that integrates audio processing and retrieval-augmented generation is innovative and addresses the limitations of existing LALMs. However, the paper could benefit from more detailed descriptions of the audio processing tool and its integration with the reasoning LLM, as well as clearer explanations of the filtering process for question validity and answer correctness.
Results & evaluation
The experimental evaluation is thorough, assessing multiple state-of-the-art LALMs against the AudioRAG benchmark. The results clearly demonstrate the challenges faced by current models, highlighting the need for improved reasoning capabilities. The comparison between raw models and the agentic pipeline provides compelling evidence of the pipeline's effectiveness. However, the paper lacks detailed statistical analyses and visualizations that could further substantiate the findings.
Reproducibility
The paper provides a GitHub repository link for the dataset, which is a positive step towards reproducibility. However, it lacks detailed implementation instructions for the agentic pipeline and the specific configurations used in experiments. This could hinder other researchers from replicating the results accurately.
Limitations
One limitation is the reliance on LLMs for generating questions and answers, which may introduce biases or inaccuracies inherent in the models. Additionally, the benchmark's scope may not cover all real-world scenarios, potentially limiting its applicability. The increase in invalid answers from the agentic pipeline suggests that the complexity of multi-hop reasoning may lead to logical errors.
Broader impact
The proposed benchmark and agentic pipeline have significant implications for enhancing audio-based reasoning systems. By addressing the challenges of integrating external knowledge with audio processing, this work could lead to more robust applications in various fields, including education, entertainment, and information retrieval systems. The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
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