Qualcomm incorporated (20240320433). SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS simplified abstract
Contents
SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS
Organization Name
Inventor(s)
Christopher Lott of San Diego CA (US)
Mingu Lee of San Diego CA (US)
Joseph Binamira Soriaga of San Diego CA (US)
Jilei Hou of San Diego CA (US)
SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320433 titled 'SPECULATIVE DECODING IN AUTOREGRESSIVE GENERATIVE ARTIFICIAL INTELLIGENCE MODELS
The present disclosure involves techniques and apparatus for generating a response to an input query using generative models.
- Generating sets of tokens based on an input query and a first generative model.
- Outputting the sets of tokens to a second generative model for verification.
- Speculatively generating additional sets of tokens while waiting for a selected set to be indicated.
- Receiving the indication of the selected set of tokens and outputting associated tokens for verification.
- Outputting the selected set of tokens as a response to the input query.
- Potential Applications:**
- Natural language processing - Chatbots and virtual assistants - Content generation for social media platforms
- Problems Solved:**
- Improving the accuracy and efficiency of response generation - Enhancing user interaction with AI systems - Streamlining the process of generating responses to queries
- Benefits:**
- Faster and more accurate responses to user queries - Enhanced user experience with AI-powered systems - Increased productivity in content generation tasks
- Commercial Applications:**
Title: AI-Powered Response Generation System This technology can be utilized in customer service chatbots, social media marketing tools, and automated content creation platforms. It has the potential to revolutionize how businesses interact with customers online, improving response times and overall user satisfaction.
- Questions about AI Response Generation:**
1. How does this technology improve the efficiency of response generation compared to traditional methods? 2. What are the key advantages of using generative models for generating responses to user queries?
Original Abstract Submitted
certain aspects of the present disclosure provide techniques and apparatus for generating a response to an input query using generative models. the method generally includes generating, based on an input query and a first generative model, a first plurality of sets of tokens. the first plurality of sets of tokens are output to a second generative model for verification. while waiting to receive an indication of a selected set of tokens from the first plurality of sets of tokens, a second plurality of sets of tokens are speculatively generated. the indication of a selected set of tokens from the first plurality of sets of tokens is received. tokens from the second plurality of sets of tokens associated with the selected set of tokens are output to the second generative model for verification, and the selected set of tokens is output as a response to the input query.
