Generative Model Integration via Intermediary Search Engine
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Solution Overview
Problem
Generative language models are deficient in providing accurate information, especially for recent data, and are not well-suited to handle private content without user consent, lacking integration with applications to generate contextually relevant outputs.
Innovation Solution
Integrating a generative model with applications like web browsers and operating systems, allowing user interaction through a side panel that provides content and metadata as prompts, ensuring user consent for private pages, and utilizing contextual information for generating accurate and relevant outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generative model is integrated with applications to provide contextual information, then the accuracy and relevance of outputs improve, but the complexity of the system increases
Solution Approach 1:
The patent introduces a search engine as an intermediary component between the user query and the generative model. The search engine retrieves relevant information from multiple sources and feeds it to the generative model, which then generates contextualized responses. This intermediary architecture allows the system to access accurate, up-to-date information without requiring the generative model itself to be complex or trained on all available data.
Solution Approach 2:
The generative model is designed to perform multiple functions: generating responses, summarizing content, extracting key information, and contextualizing data based on the retrieved search results. This multi-functionality allows a single integrated system to handle various information processing tasks without requiring separate specialized models for each function, thereby managing complexity while improving output quality.
2Measurement precision
If a generative model is trained on comprehensive and updated data, then the accuracy of recent information improves, but the time required for training and retraining increases
Solution Approach 1:
The system performs preliminary information retrieval through the search engine before generating responses. By pre-fetching relevant information from multiple sources and indexing it, the system can quickly access accurate and up-to-date data without needing to retrain the generative model continuously. This preliminary action separates the training phase from the operation phase, allowing the model to remain static while information remains updated.
Solution Approach 2:
The system incorporates feedback loops where search results are used to verify and update the information provided to the generative model. This feedback mechanism allows the system to correct inaccuracies and incorporate recent information without requiring model retraining, as the feedback is processed through the search engine and integrated into the prompt rather than changing the model weights.
3Adaptability or versatility
If a generative model is provided with access to private content without consent, then the versatility and functionality improve, but the privacy and security risks increase
Solution Approach 1:
The system implements a feedback mechanism where user consent is explicitly sought before processing private content. The interface provides clear indications to users about what information will be processed, and users must affirmatively consent. This feedback loop gives users control over their private information, allowing the system to maintain versatility while mitigating privacy risks through informed user agreement.
Solution Approach 2:
The system applies different quality levels of information processing based on the sensitivity and nature of the content. Private content requires explicit user authorization and enhanced security measures, while public content can be processed more freely. This local quality approach allows the system to maintain high functionality for authorized content while protecting user privacy for sensitive information through differentiated handling protocols.
Data Source
AI summary
A computing system described herein includes a processor and memory storing instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model, where the prompt includes content of a webpage being presented to the user. The acts also include providing the prompt as input to the generative language model. The acts further include receiving output from the generative language model, where the generative language model generated the output based upon the prompt. The acts additionally include causing the output to be presented to the user by way of a client computing device concurrently with the webpage being presented to the user.