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 have shown promise for human-like conversations%2C they are primarily pre-trained on text data. Incorporating audio or video improves performance%2C but collecting large-scale multimodal data and pre-training multimodal LLMs is challenging. To this end%2C we propose a Fusion Low Rank Adaptation (FLoRA) technique that efficiently adapts a pre-trained unimodal LLM to consume new%2C previously unseen modalities via low rank adaptation. For device-directed speech detection%2C using FLoRA%2C the multimodal LLM achieves 22%25 relative reduction in equal error rate (EER) over the text-only approach and attains performance parity with its full fine-tuning (FFT) counterpart while needing to tune only a fraction of its parameters. Furthermore%2C with the newly introduced adapter dropout%2C FLoRA is robust to missing data%2C improving over FFT by 20%25 lower EER and 56%25 lower false accept rate. The proposed approach scales well for model sizes from 16M to 3B parameters.?quality=80&w=800)
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