Grid-Based LLM Highlighting for Multi-Source Prompt Analysis
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Solution Overview
Problem
Existing generative models, such as large language models (LLMs), are limited by their context window, requiring multiple prompts for large documents and constraining complex reasoning tasks, making them inefficient for analyzing multiple sources and generating large datasets.
Innovation Solution
A grid-based data structure and matrix user interface that allows users to apply column prompts across multiple sources, overcoming context window limitations by breaking down documents into chunks and iteratively generating context window prompts, and providing pre-generated prompts and automated generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a chat user interface is used for interacting with LLMs, then users can provide prompts and receive responses, but the interface becomes ineffective for performing multiple prompts across multiple sources of data
Solution Approach 1:
The system segments the interaction by introducing a grid-based interface that separates multiple prompts and multiple data sources into distinct rows and columns. Each cell represents a specific prompt-source combination, allowing users to systematically manage multiple prompts across multiple sources rather than handling them sequentially through a chat interface.
Solution Approach 2:
The patent transitions from a one-dimensional chat interface (single conversation thread) to a two-dimensional grid interface where one dimension represents prompts and the other represents data sources. This dimensional expansion enables simultaneous organization and execution of multiple prompts across multiple sources, resolving the limitation of traditional chat interfaces.
2Device complexity
If standard chat interfaces are used, then single dialogue threads are maintained, but the complexity of multi-step reasoning tasks is constrained
Solution Approach 1:
The grid interface segments complex multi-step reasoning tasks into individual cells, where each cell can represent a specific reasoning step or data extraction task. This allows the system to break down complex reasoning into manageable units that can be processed systematically across multiple prompts and sources, improving both handling capability and execution reliability.
3Speed
If LLM context window limitations are considered, then inference speed is maintained for shorter inputs, but the number of sources and prompts that can be analyzed is limited
Solution Approach 1:
The system segments large datasets into smaller units that fit within LLM context windows, organizing them in a grid structure. This allows the LLM to process multiple smaller prompts across multiple sources in parallel or sequential batches, maintaining inference speed while significantly increasing the total number of sources and prompts that can be analyzed compared to a single large context window approach.
Data Source
AI summary
A data analytics system uses a grid-based data structure to improve the usability of LLMs in the analysis of large data sets, to synthesize information for use in other generative AI contexts, and to improve a user's ability to interface with an LLM. A grid-based data structure is a data structure or database that stores the results of column prompts applied to sources. The grid-based data structure may store the results in a relational manner. For example, a grid-based data structure may have rows that correspond to sources (e.g., documents, files, or databases) and columns that correspond to prompts. Each cell of the grid-based data structure stores the output of the column prompt applied to a source using an LLM. Thus, each column prompt may be systematically applied to each source to generate information based on the sources in an organized way.