Semantic Decomposition and Selective Context Filtering -- Text Processing Techniques for Context-Aware NLP-Based Systems
This work addresses the need for more efficient and contextually relevant processing in NLP-based systems, though it appears incremental as it builds on existing context-aware techniques.
The paper tackles the problem of improving context-aware NLP systems by introducing Semantic Decomposition to structure input prompts hierarchically and Selective Context Filtering to remove irrelevant context, aiming to enhance LLM interfaces, response cohesion, and workflow optimization.
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.