Chain-of-Thought Response Engine for Multi-Step Reasoning
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Solution Overview
Problem
Generative response engines struggle with multi-step reasoning tasks, such as mathematical proofs, due to their autoregressive models predicting each token based on previous ones without retaining intermediate steps, leading to inefficiencies in complex problem solving and lack of transparency.
Innovation Solution
Implementing a chain-of-thought (CoT) reasoning model that combines reinforcement learning and chain-of-thought reasoning to generate reasoning tokens, which are used to refine the response process, allowing for explicit breakdown of complex problems into manageable components and providing a summary of the reasoning steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autoregressive models predict each token based on previous ones, then the model can adapt quickly to new inputs, but the model loses intermediate reasoning steps and cannot perform multi-step reasoning effectively
Solution Approach 1:
The patent segments the reasoning process into distinct components by introducing a separate reasoning model that generates intermediate reasoning steps (second tokens) independently from the main generative model. This segmentation allows the system to preserve and process intermediate reasoning steps separately, enabling multi-step reasoning while maintaining the adaptability of the original autoregressive model for new inputs.
Solution Approach 2:
The patent introduces an intermediary reasoning model that acts as a mediator between the input prompt and the final response. This reasoning model generates intermediate reasoning steps that bridge the gap between the initial input and the final output, allowing the system to perform multi-step reasoning without compromising the adaptability of the main generative model.
2Reliability
If the model generates intermediate reasoning steps, then multi-step reasoning capability is improved, but the complexity of the system increases
Solution Approach 1:
The patent merges two specialized models (a reasoning model and a generative model) into a coordinated system where each model performs its specific function. The reasoning model generates intermediate steps while the generative model produces the final response, combining their capabilities to achieve reliable multi-step reasoning without requiring a single overly complex model.
Solution Approach 2:
The patent creates a universal system architecture that can handle both simple one-step tasks and complex multi-step reasoning tasks through the same framework. The reasoning model and generative model work together to provide a multi-functional solution that adapts to different task complexities without requiring separate specialized systems.
3Loss of information
If reasoning tokens are provided to the requester, then transparency is improved, but the response time and processing overhead increase
Solution Approach 1:
The patent implements partial transparency by allowing the requester to optionally receive intermediate reasoning steps. The system can provide full reasoning transparency when needed for verification or learning purposes, while also supporting faster responses when the requester only needs the final result, thus balancing transparency with response time requirements.
Solution Approach 2:
The patent creates a dynamic system where the level of reasoning transparency can be adjusted based on the requester's needs. The system can switch between providing full intermediate reasoning steps and providing only final responses, allowing flexible adaptation to different transparency requirements without permanently increasing response time for all requests.
Data Source
AI summary
The present technology pertains to a generative response system (system) that includes a chain-of-thought (CoT) reasoning model. The system receives a prompt for a response, wherein the response benefits from multi-step, CoT reasoning. The prompt is tokenized to generate input tokens, which can also include tokens representing a contextual conversation history. A first machine learning (ML) model having a CoT functionality processes the input tokens, generating reasoning tokens, which explore one or more reasoning frameworks for responding to the prompt. The combination of the first and second tokens is processed to generate output tokens representing the response sent to the requester. The second tokens are not provided to the requester and are omitted from the chat history. However, a summary of the multi-step reasoning framework used to generate the response can be generated based on the second tokens and presented to the requester.