Multimodal Large Language Models with Fusion Low Rank Adaptation for Device Directed Speech Detection
Autor: | Palaskar, Shruti, Rudovic, Oggi, Dharur, Sameer, Pesce, Florian, Krishna, Gautam, Sivaraman, Aswin, Berkowitz, Jack, Abdelaziz, Ahmed Hussen, Adya, Saurabh, Tewfik, Ahmed |
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Rok vydání: | 2024 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted at Interspeech 2024 |
Databáze: | arXiv |
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