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MUGEN: Evaluating and Improving Multi-audio Understanding of Large Audio-Language Models
arXiv – CS AI|Chih-Kai Yang, Yun-Shao Tsai, Yu-Kai Guo, Ping-Le Tsai, Yen-Ting Piao, Hung-Wei Chen, Ting-Lin Hsiao, Yun-Man Hsu, Ke-Han Lu, Hung-yi Lee|
🤖AI Summary
Researchers introduce MUGEN, a comprehensive benchmark revealing significant weaknesses in large audio-language models when processing multiple concurrent audio inputs. The study shows performance degrades sharply with more audio inputs and proposes Audio-Permutational Self-Consistency as a training-free solution, achieving up to 6.74% accuracy improvements.
Key Takeaways
- →MUGEN benchmark exposes fundamental limitations in current large audio-language models for multi-audio understanding.
- →Model performance degrades significantly as the number of concurrent audio inputs increases, highlighting input scaling as a major bottleneck.
- →Audio-Permutational Self-Consistency strategy improves accuracy by up to 6.28% without requiring additional training.
- →Combining permutation strategies with Chain-of-Thought reasoning further boosts performance to 6.74% improvement.
- →The research identifies critical blind spots in current LALMs across speech, general audio, and music domains.
#audio-language-models#mugen-benchmark#multi-audio#ai-research#model-performance#chain-of-thought#audio-processing#machine-learning
Read Original →via arXiv – CS AI
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