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AI Research
arXiv CS.AI·May 26·5 min read
EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs
TL;DR
Audio Large Language Models (ALLMs) struggle with real-world noise, leading to errors in understanding and generating responses. EchoDistill is a self-distillation framework that aligns noisy audio inputs with clean audio references to improve model robustness.
✦ Why It Matters
Engineers can implement EchoDistill to enhance the robustness of audio models against real-world noise without extra computational costs.
Key Takeaways
- Implement EchoDistill for improved audio model robustness.
- Utilize frozen teacher models for better alignment.
- Optimize noisy responses using group-relative policy optimization.
- Achieve higher performance without additional inference costs.
Full Summary
Audio Large Language Models (ALLMs) are often compromised by real-world noise, which can cause significant semantic drift and hallucinations in their outputs. To tackle this issue, EchoDistill was developed as an alignment-based noisy-to-clean self-distillation framework.
It utilizes a frozen clean-audio teacher model to guide a noisy-audio student model during inference. The student generates candidate responses in noisy conditions, which are then optimized using group-relative policy optimization (GRPO), rewarding token-level consistency with the teacher's outputs.
Experimental results demonstrated that EchoDistill improved semantic reliability and task performance, achieving an average increase of 4.18% in Generalized Semantic Reliability (GSR) compared to the strongest baseline. Additionally, ablation studies showed further improvements in accuracy and performance metrics.
These findings suggest that EchoDistill can enhance ALLMs' robustness without incurring additional inference costs.