In-Context Semantic Ensemble for Helpful Document Ranking
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Solution Overview
Problem
Conventional document retrieval systems using machine learning models struggle to accurately identify documents that provide rich and helpful information relevant to user queries, often failing to evaluate the content's relevance effectively.
Innovation Solution
An ensemble model that combines a semantic search model for ranking documents based on semantic similarity with a reasoning model for evaluating the content's relevance, incorporating a rule-based model to filter documents, enhances the accuracy of document retrieval by generating prompts for in-context learning and understanding the query-document relationship.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a semantic search model is used to retrieve documents based on semantic similarity, then the retrieval speed and coverage are improved, but the accuracy of identifying helpful and relevant information deteriorates
Solution Approach 1:
The document retrieval system is segmented into multiple specialized components: a semantic search model for fast retrieval based on similarity, a reasoning model for evaluating helpfulness and relevance, and an ensemble model for final decision-making. Each component performs a specific function, allowing the system to maintain fast retrieval while improving accuracy through specialized evaluation.
Solution Approach 2:
A reasoning model acts as an intermediary between the semantic search model and the final retrieval decision. The reasoning model receives documents from the semantic search model and evaluates whether they are helpful and relevant, serving as a mediator that bridges fast retrieval with accurate evaluation.
2Device complexity
If conventional machine learning models are used for document retrieval, then the system complexity is reduced, but the ability to evaluate content relevance effectively deteriorates
Solution Approach 1:
The system merges multiple models with different capabilities: a semantic search model for similarity-based retrieval, a reasoning model for content evaluation, and an ensemble model for integration. This combination allows the system to achieve sophisticated content relevance evaluation while managing complexity through modular architecture.
Solution Approach 2:
The ensemble model serves multiple functions: it integrates results from the semantic search model, incorporates reasoning model evaluations, and makes final retrieval decisions. This multi-functionality allows a single component to handle complex evaluation tasks that would otherwise require multiple separate systems.
3Ease of operation
If only semantic similarity is used for document ranking, then the retrieval process is simplified and faster, but the quality of relevant information deteriorates
Solution Approach 1:
The semantic search model performs preliminary retrieval of potentially relevant documents based on semantic similarity. This preliminary action filters the large document corpus into a manageable set that can then be evaluated by the reasoning model, maintaining simplicity in the initial retrieval process while improving quality through subsequent evaluation.
Solution Approach 2:
The reasoning model provides feedback on whether retrieved documents are helpful and relevant, and this feedback is incorporated into the ensemble model's final decisions. This feedback mechanism ensures that documents meeting semantic similarity criteria are further validated for quality, improving reliability while keeping the process manageable.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.