Google llc (20240256965). Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps simplified abstract

Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps

Organization Name

google llc

Inventor(s)

Hyung Won Chung of Mountain View CA (US)

Barret Zoph of San Francisco CA (US)

Dengyong Zhou of Redmond WA (US)

Liam Fedus of Palo Alto CA (US)

Shayne Longpre of Surrey (CA)

Le Hou of South Setauket NY (US)

Yi Tay of Singapore (SG)

Jason Weng Wei of Mountain View CA (US)

Siddhartha Brahma of San Jose CA (US)

Quoc V. Le of Sunnyvale CA (US)

Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240256965 titled 'Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps

The abstract describes a method for training a machine-learned sequence processing model by obtaining training examples, inputting queries to the model, evaluating responses and traces, and updating model parameters based on the evaluations.

• Obtaining a plurality of training examples for the machine-learned sequence processing model
• Inputting queries associated with each training example to the model
• Evaluating responses and traces from the model using ground truth data
• Updating model parameters based on the evaluations

Potential Applications: - Natural language processing - Speech recognition - Sentiment analysis - Machine translation

Problems Solved: - Improving the accuracy of sequence processing models - Enhancing the performance of machine learning algorithms - Streamlining the training process for complex models

Benefits: - Increased efficiency in training machine learning models - Higher accuracy in processing sequences - Improved performance in various applications

Commercial Applications: Title: Enhanced Sequence Processing Model Training for AI Applications This technology can be used in industries such as: - Healthcare for medical record analysis - Finance for fraud detection - E-commerce for personalized recommendations

Prior Art: Researchers can explore prior studies on machine learning training methods and sequence processing models to understand the evolution of this technology.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms, natural language processing techniques, and sequence processing model training methods to enhance the performance of AI applications.

Questions about Machine-Learned Sequence Processing Model Training: 1. How does this method improve the training process for machine-learned sequence processing models? 2. What are the key factors that influence the effectiveness of updating model parameters based on response and trace evaluations?

Original Abstract Submitted

an example method for training a machine-learned sequence processing model includes obtaining a plurality of training examples for training the machine-learned sequence processing model. for each respective training example of the plurality of training examples, the example method includes: obtaining a respective query associated with the respective training example; inputting the respective query to the machine-learned sequence processing model; obtaining, from the machine-learned sequence processing model a response to the respective query and a trace of intermediate states from the respective query to the response; evaluating the response using a ground truth response associated with the respective training example; evaluating the trace using a ground truth trace associated with the respective training example; and updating one or more parameters of the machine-learned sequence processing model based on the evaluation of the response and based on the evaluation of the trace.