In a three-stage end-to-end spoken language model, experience replay (mixing old data into later training) was the most effective mitigation against catastrophic forgetting, greatly outperforming model merging and LoRA scaling discounting.
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Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models
In a three-stage end-to-end spoken language model, experience replay (mixing old data into later training) was the most effective mitigation against catastrophic forgetting, greatly outperforming model merging and LoRA scaling discounting.