Multimodal Large Language Models with Fusion Low Rank Adaptation for Device Directed Speech Detection

• URL: http://arxiv.org/abs/2406.09617v1
• Date: Thu, 13 Jun 2024 22:52:07 GMT
• Title: Multimodal Large Language Models with Fusion Low Rank Adaptation for Device Directed Speech Detection
• Authors: Shruti Palaskar, Oggi Rudovic, Sameer Dharur, Florian Pesce, Gautam Krishna, Aswin Sivaraman, Jack Berkowitz, Ahmed Hussen Abdelaziz, Saurabh Adya, Ahmed Tewfik,
• Abstract summary: Large Language Models (LLMs) have shown promise for human-like conversations but are primarily pre-trained on text data. We propose a Fusion Low Rank Adaptation (FLoRA) technique that efficiently adapts a pre-trained unimodal LLM to consume new, previously unseen modalities. For device-directed speech detection, using FLoRA, the multimodal LLM achieves 22% relative reduction in equal error rate (EER) over the text-only approach.
• Score: 8.683288452838136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Large Language Models (LLMs) have shown promise for human-like conversations, they are primarily pre-trained on text data. Incorporating audio or video improves performance, but collecting large-scale multimodal data and pre-training multimodal LLMs is challenging. To this end, we propose a Fusion Low Rank Adaptation (FLoRA) technique that efficiently adapts a pre-trained unimodal LLM to consume new, previously unseen modalities via low rank adaptation. For device-directed speech detection, using FLoRA, the multimodal LLM achieves 22% 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, with the newly introduced adapter dropout, FLoRA is robust to missing data, improving over FFT by 20% lower EER and 56% lower false accept rate. The proposed approach scales well for model sizes from 16M to 3B parameters.

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