LARGE LANGUAGE MODEL AGGREGATOR

Smart overview of the Invention

The patent application outlines a system for aggregating multiple Large Language Model Services (LLMSs) to conduct conversations. The method involves selecting appropriate LLMSs based on user-provided profile information and conversation types. It then facilitates interaction through a graphical user interface, allowing users to input prompts and receive responses from various LLMSs simultaneously. This approach enhances user convenience by consolidating the capabilities of multiple language models into a single interface.

Background

Generative artificial intelligence encompasses techniques for creating content, with Large Language Models (LLMs) being a prominent application. LLMs are designed to understand and generate human language using vast datasets and complex algorithms. They perform tasks like language generation, translation, and data analysis. By integrating LLMs with other AI models, multimodal systems can generate text, images, or audio based on diverse inputs. This innovation leverages the strengths of LLMs to provide coherent and contextually relevant outputs across different media.

Methodology

The proposed method involves accessing user profile information and selected terms to determine the conversation type. It identifies suitable LLMSs for participation in the conversation, renders a user interface with dialog boxes for input, and communicates prompts to the selected LLMSs. Responses are received and displayed with indicators showing which LLMS provided each response. This system allows users to engage multiple LLMSs in parallel, facilitating richer interactions and comparisons of responses.

Advanced Features

Additional features include sharing profile information with LLMSs to tailor responses, offering menu options for selecting conversation types like image generation or text-based discussions, and ranking responses based on user preferences or service ratings. The system can handle subsequent prompts by maintaining context from previous interactions. It also supports collaboration between LLMSs that agree to share responses, enhancing the overall conversational experience.

Implementation

The system comprises processors and computer-readable media storing instructions to execute the described methods. These techniques can be implemented in various contexts, providing flexibility for different applications. The aggregation of LLMSs benefits users by offering diverse perspectives in a single interface while also benefiting LLMSs through increased traffic and improved machine learning data.