LASER Context Refinement in Long-Document RAG Pipelines
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Solution Overview
Problem
Large Language Models (LLMs) face challenges in processing long documents, particularly in maintaining coherence and performing long-range reasoning, leading to a noticeable drop in performance as the length of the input context increases, which is critical in domains like legal, engineering, and healthcare.
Innovation Solution
The system employs an iterative attention focusing technique (LASER) to refine and condense document context, incorporating advanced document processing and intelligent information retrieval (SCORE-RAG) to enhance model comprehension and coherence over extended inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If LLMs process long documents directly, then they can handle extended context, but their performance drops noticeably as input length increases
Solution Approach 1:
The patent divides long documents into smaller, manageable chunks or segments. Each segment is processed independently by the LLM, which maintains high performance on shorter inputs. The segments are then recombined to form the complete understanding of the long document, effectively resolving the performance degradation issue while maintaining the ability to handle extended context.
2Use of energy by moving object
If LLMs are trained on limited data, then they achieve computational efficiency, but they struggle with long-range reasoning and coherence in long documents
Solution Approach 1:
The patent performs preliminary processing of long documents by identifying and extracting key segments, relationships, and structures before feeding them to the LLM. This pre-processing step creates a condensed representation that preserves long-range dependencies and coherence information, allowing the computationally efficient LLM to perform well on long-range reasoning tasks without requiring extensive retraining.
Data Source
AI summary
A system and method of improving performance of LLMs including receiving context files, generating refined context files from the context files, sending the refined context files to h-LLMs, receiving a user prompt, generating a plurality of derived prompts from the user prompt, transmitting the plurality of derived prompts to the h-LLMs, receiving a plurality of h-LLM results, processing the plurality of h-LLM results to generate a responsive result, and transmitting the responsive result to a user interface.