AnyAudio-Judge: A Dynamic Rubric-Based Benchmark and Evaluator for Audio Instruction Following
arXiv:2606.03116v1 Announce Type: cross Abstract: The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust alignment evaluation. Current automated evaluation methods heavily rely on holistic scoring from general-purpose large language models, which struggle to decouple complex instructions, lack interpretability, and fail to capture fine-grained attribute mismatches. To address this, we introduce a novel dynamic rubric-based evaluation paradigm that adaptively decomposes complex audio captions into a variable number of independent, verifiable bi
The rapid advancement of instruction-guided audio generation models necessitates more robust and interpretable evaluation methods to ensure alignment with complex user instructions.
Improved evaluation for AI-generated audio is critical for developing more reliable and sophisticated audio AI, impacting areas from content creation to human-computer interaction.
The introduction of dynamic, rubric-based evaluation provides a more granular and interpretable method for assessing AI audio generation, moving beyond holistic scoring.
- · Audio AI developers
- · AI evaluation researchers
- · Content creators using audio AI
- · Developers reliant solely on holistic LLM-based evaluation
More accurate and nuanced feedback for training audio generation models.
Faster iteration and improvement cycles for AI audio capabilities, leading to more realistic and controllable synthetic audio.
Enhanced trust and broader adoption of AI-generated audio in professional and creative fields.
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Read at arXiv cs.AI