How Contrastive Decoding Enhances Large Audio Language Models
While Contrastive Decoding (CD) has been proposed to enhance Large Audio Language Models (LALMs), it has not been evaluated at scale, and the underlying mechanisms driving its success remain unclear. This study systematically evaluates four distinct CD strategies across diverse LALM architectures. We identify Audio-Aware Decoding and Audio Contrastive Decoding as the most effective methods. However, their impact varies significantly across models. To explain this variability, we profile the baseline error composition of each model and measure how readily contrastive decoding corrects each error type. Our analysis demonstrates that CD reliably rectifies errors in which models falsely claim...
| Authors | Tzu-Quan Lin, Wei-Ping Huang, Yi-Cheng Lin et al. |
|---|---|
| Primary category | cs.SD |
| Published | 10 March 2026 |
| Last updated at source | 13 September 2026 |
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