Context Briefs for LLM Structured Data Processing

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Solution Overview

Problem

Large language models (LLMs) struggle to effectively process structured data, often resulting in hallucinations and incorrect responses due to a lack of understanding of data structure and context ambiguity, especially with extensive and complex datasets.

Innovation Solution

The use of context briefs, which integrate dynamic structured data with descriptive content, is generated using context templates to provide relevant context to LLMs, enabling them to generate more accurate and tailored responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If LLMs process structured data directly, then they can handle data efficiently, but they produce hallucinations and incorrect responses due to lack of context understanding

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidresponse accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent introduces context briefs as an intermediary component between the structured data and the LLM. These briefs contain natural language descriptions and contextual information that bridge the gap between machine-readable structured data and the LLM's natural language processing capabilities, thereby reducing hallucinations while maintaining processing efficiency

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary processing by generating context briefs before feeding data to the LLM. This preliminary action includes creating natural language summaries, adding contextual metadata, and preparing disambiguated representations of structured data, which enables the LLM to process information more accurately without sacrificing efficiency

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If LLMs are provided with extensive datasets, then they have more information to work with, but they struggle with context ambiguity and data structure understanding

Engineering Contradiction:

Improvedata volumeVSAvoidcontext disambiguation difficulty

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The patent segments extensive datasets into manageable context briefs that are tailored to specific queries. Each context brief contains only the relevant structured data and contextual information needed for a particular task, making it easier for the LLM to process and disambiguate information without being overwhelmed by the full dataset

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by providing different types of contextual information for different parts of the data based on specific needs. Context briefs are customized with relevant metadata, disambiguated terms, and targeted descriptions that match the specific query requirements, enabling precise context understanding rather than generic data processing

Inventive Principle:

Principle #3Local quality

3Adaptability or versatility

If context briefs are generated with dynamic data and descriptive content, then response relevance improves, but system complexity increases

Engineering Contradiction:

Improveresponse customizationVSAvoidcontext brief generation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a universal context brief generation framework that handles multiple data types, query formats, and contextual requirements through a single standardized process. The context brief template and generation mechanism are designed to be multi-functional, accommodating various structured data formats and query types without requiring separate complex systems for each case

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250138910A1Generating and using context briefs to identify relevant chat responses

Publication Date: 2025.05.01 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Methods, computer systems, and computer-storage media are provided for generating and using context briefs to identify relevant chat responses. In embodiments, a context template associated with an intent is obtained. The context template includes a data reference referencing dynamic data and content providing context for the dynamic data. Thereafter, a context brief associated with the intent is generated by obtaining the dynamic data and incorporating the dynamic data with the content. Upon obtaining an input data request indicating a user intent, the context brief is identified as corresponding with the user intent of the input data request based on the intent associated with the context brief. A prompt to be input into a large language model is generated. The prompt includes the input data request and the context brief. A response relevant to the input data request is obtained as output from the large language model.