Summary of Distilling An End-to-end Voice Assistant Without Instruction Training Data, by William Held et al.
Distilling an End-to-End Voice Assistant Without Instruction Training Data
by William Held, Ella Li, Michael Ryan, Weiyan Shi, Yanzhe Zhang, Diyi Yang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach to developing voice assistants has been proposed, aiming to improve the modeling of audio and text simultaneously. This is achieved by training End-to-End Speech Large Language Models (LLMs) using Supervised Finetuning (SFT). The proposed method seeks to overcome the limitations of current models, which often treat audio and text as separate entities, leading to a loss of valuable speech information and increased complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Voice assistants like Siri and Google Assistant typically process audio and text separately. This can lead to missing important details in speech and making it harder for AI systems to understand what we’re saying. Researchers have been trying to find ways to improve this by training special kinds of artificial intelligence models, called Large Language Models (LLMs). These LLMs are trained using a technique called Supervised Finetuning (SFT), which helps them learn to recognize patterns in audio and text at the same time. |
Keywords
» Artificial intelligence » Supervised
