Image Analysis With Chain-of-Thought Prompting for Complex Reasoning
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Solution Overview
Problem
Existing machine-learned models struggle with tasks that require slow and deliberate thinking, such as logical, mathematical, and commonsense reasoning, even when scaled to hundreds of billions of parameters.
Innovation Solution
The proposed method involves constructing prompting inputs for machine-learned models using a 'chain of thought' approach, which includes an instructive sequence descriptive of an instructive query, response, and trace of intermediate states. This technique allows the model to decompose complex queries into intermediate steps, focusing processing power on challenging steps rather than spreading it thin over all steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine-learned models are scaled to hundreds of billions of parameters, then model capacity increases, but computational resource requirements increase and the models still struggle with complex reasoning tasks
Solution Approach 1:
The patent segments complex reasoning tasks into intermediate steps through chain-of-thought prompting. Instead of requiring the model to process entire complex queries in one step, the system breaks down the reasoning process into manageable intermediate states, allowing the model to handle complex tasks with smaller parameter counts while maintaining high computational efficiency
2Speed
If machine-learned models process complex queries in a single step, then processing speed is maintained, but accuracy deteriorates on tasks requiring deliberate thinking
Solution Approach 1:
The patent segments complex query processing into multiple intermediate reasoning steps. The system generates a chain of thought that breaks down complex queries into simpler sub-steps, allowing the model to maintain processing speed while improving accuracy through structured intermediate reasoning states
Solution Approach 2:
The patent applies preliminary action by generating intermediate reasoning steps before arriving at the final answer. The system prepares the reasoning path in advance through chain-of-thought sequences, enabling the model to approach complex tasks with pre-processed logical frameworks that improve accuracy without sacrificing overall processing efficiency
3Productivity
If machine-learned models spread processing power thin over all steps, then computational efficiency is maintained, but performance on challenging steps deteriorates
Solution Approach 1:
The patent segments the reasoning process into distinct intermediate steps, allowing the model to concentrate computational resources on specific challenging steps rather than distributing power uniformly. Each intermediate state in the chain of thought can be processed with focused attention, improving reliability on difficult reasoning steps while maintaining overall computational efficiency through the structured breakdown
Data Source
AI summary
An example technique for image analysis is provided. An example image analysis method includes obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example image analysis method includes inputting, to a machine-learned model, the instructive sequence and an operative image processing query that comprises image data, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative image processing response that comprises an analysis of the image data.