When the Same Musical Knowledge Forgets Differently: A Clean Probe of Pathway-Dependent Forgetting

2023-03-30 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new study reveals that knowledge acquired via text pathways is significantly more prone to forgetting than audio-pathway knowledge in multimodal models, challenging the Pathway-Invariant Assumption. Researchers introduced the Paired Pathway Controlled Protocol (PPCP) to cleanly test this phenomenon, ensuring target equivalence, symmetric supervision, and acquisition comparability. Across four architecturally distinct audio-language models, including Qwen2-Audio-7B-Instruct, SALMONN, Audio Flamingo 3, and Qwen2.5-Omni, a consistent asymmetry was observed: text-pathway knowledge was forgotten more, with generation-space gaps (ΔDgen) ranging from +0.029 to +0.106 and logit-space Relative Retention Shift (RRS) up to +0.423. Six targeted controls ruled out alternative explanations like architectural depth or training factors, indicating the effect is tied to input representation.

Key takeaway

For Research Scientists developing multimodal AI, this study reveals that knowledge acquired via text pathways is significantly more prone to forgetting than audio-pathway knowledge under adaptation. You should account for this pathway-dependent forgetting in model design and continual learning strategies, potentially requiring pathway-specific resource allocation or targeted unlearning methods to maintain robust performance across modalities.

Key insights

Knowledge acquisition route significantly impacts forgetting vulnerability in multimodal models, with text-pathway knowledge being more susceptible.

Principles

• The Pathway-Invariant Assumption is false for multimodal models.
• Forgetting is highly route-dependent, not just content or adaptation pressure.
• Input representation, not architectural depth, drives forgetting asymmetry.

Method

The Paired Pathway Controlled Protocol (PPCP) is a three-phase framework ensuring target equivalence, symmetric supervision, and acquisition comparability for pathway-level forgetting analysis.

In practice

• Design continual learning interventions to be pathway-aware.
• Allocate resources pathway-specifically for multimodal adaptation.
• Do not assume model editing transfers across acquisition pathways.

Topics

• Pathway-Dependent Forgetting
• Multimodal Models
• Catastrophic Forgetting
• Audio-Language Models
• Continual Learning
• Music Understanding

Code references

• Ameame1/Audio_Memory_PPCP

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• When the Same Musical Knowledge Forgets Differently: A Clean Probe of Pathway-Dependent Forgetting — The acquisition route of knowledge (e.g., text vs. audio) significantly influences how easily it is forgotten in multimodal models.
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• Why Fine-Tuning Encourages Hallucinations and How to Fix It — SFT-induced hallucinations stem from factual forgetting due to semantic interference, mitigable by self-distillation or parameter freezing.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.