Knowledge Base Answer Re-Ranking with LLM Context Analysis
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Solution Overview
Problem
Existing knowledge-based question-answering systems fail to accurately rank answers based on the context and intent of user queries, often prioritizing documents with matching phrases over those that are more relevant to the user's intent.
Innovation Solution
A knowledge-based question-answering system that utilizes large language models (LLMs) in a re-ranking process, incorporating multiple re-rankers and graders to evaluate the context and intent of user queries, generating a final response that includes both LLM-generated answers and graded document citations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If answers are ranked based on matching phrases between documents and user query, then the ranking process is simple and fast, but the accuracy of answer ranking deteriorates because context and intent are not considered
Solution Approach 1:
The ranking process is divided into multiple stages: initial retrieval based on phrase matching, followed by re-ranking using LLMs that analyze context and intent. This segmentation allows the system to maintain speed in the initial phase while improving accuracy in subsequent phases.
Solution Approach 2:
Large language models are introduced as intermediary components between the simple phrase-matching retrieval and the final ranking. These LLMs act as mediators that analyze context and intent, transforming the raw matching results into accurately ranked answers without requiring the entire process to be complex from the start.
2Measurement precision
If multiple re-rankers and graders are used to evaluate context and intent, then the accuracy of answer ranking improves, but the device complexity increases
Solution Approach 1:
The complex evaluation task is segmented into multiple specialized components (re-rankers and graders), each responsible for specific aspects of context and intent analysis. This modular segmentation makes the overall complex system more manageable and allows for targeted optimization of each component.
Solution Approach 2:
The LLM-based re-rankers and graders serve multiple functions simultaneously: they evaluate context, analyze intent, rank answers, and provide explanations. This multi-functionality reduces the need for separate specialized components for each task, thereby managing complexity while maintaining high accuracy.
3Measurement precision
If large language models are applied to re-rank answers, then the consideration of context and intent improves, but the computational resources and time required increase
Solution Approach 1:
The system performs preliminary phrase-matching retrieval to identify candidate answers before applying computationally intensive LLM-based re-ranking. This preliminary action filters out obviously irrelevant documents, reducing the number of candidates that require time-consuming LLM analysis and thereby reducing overall processing time.
Solution Approach 2:
Instead of applying LLM-based re-ranking to all retrieved documents, the system applies it selectively to a subset of top-candidate answers. This partial action approach maintains high accuracy for the most relevant answers while avoiding the excessive time cost of analyzing every single retrieved document with full LLM capability.
Data Source
AI summary
Aspects of the subject disclosure may include systems and methods, for example, including receiving a user input in natural language, retrieving a first answer including a list of a first number of documents relevant to the user input by searching indexed documents in a knowledge base, applying the first answer to a large language model reader, resulting in a second answer, re-ranking the first answer by using the second answer, resulting in a third answer including a re-ranked list of the first number of documents, and generating a final response including the second answer and one or more documents among the third answer.