Semantic Decomposition and Selective Context Filtering -- Text Processing Techniques for Context-Aware NLP-Based Systems

TL;DR

This paper introduces Semantic Decomposition and Selective Context Filtering techniques to enhance context-aware NLP systems by structuring input prompts and filtering irrelevant information, thereby improving response relevance and workflow efficiency.

Contribution

The paper presents novel techniques for structuring and filtering context in NLP systems, enabling better integration with large language models and more efficient automated workflows.

Findings

Semantic Decomposition improves prompt parsing and processing.

Selective Context Filtering enhances response relevance.

Techniques facilitate dynamic LLM-to-system interfaces.

Abstract

In this paper, we present two techniques for use in context-aware systems: Semantic Decomposition, which sequentially decomposes input prompts into a structured and hierarchal information schema in which systems can parse and process easily, and Selective Context Filtering, which enables systems to systematically filter out specific irrelevant sections of contextual information that is fed through a system's NLP-based pipeline. We will explore how context-aware systems and applications can utilize these two techniques in order to implement dynamic LLM-to-system interfaces, improve an LLM's ability to generate more contextually cohesive user-facing responses, and optimize complex automated workflows and pipelines.