Retrieval-Augmented Q&A With Offline Language Unification

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Solution Overview

Problem

In crosslingual document retrieval and answer generation using large models, the presence of documents in different languages leads to low matching accuracy and high computational load due to language differences.

Innovation Solution

A retrieval-augmented query-and-answer method that involves character-level and semantic-level matching to screen material knowledge points, generating prompt information based on bound material slices, and inputting it into a query-and-answer large model, with offline storage of material knowledge points in a standard language derived from non-standard language slices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If documents in different languages are directly input into the large model for crosslingual retrieval, then the system can handle multilingual queries, but matching accuracy decreases and computational load increases

Engineering Contradiction:

Improvemultilingual query handlingVSAvoidmatching accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent applies preliminary action by translating material knowledge points from non-standard languages to standard language during offline database construction. This pre-translation prepares the data in advance, so that during online query processing, no real-time translation is needed, thus maintaining high matching accuracy while handling multilingual queries efficiently

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces standard language as an intermediary between non-standard language materials and the large model. Material knowledge points in various languages are first translated to standard language and stored in the database, then matched with queries in standard language, avoiding direct crosslingual matching and thereby maintaining high accuracy

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If documents in different languages are directly input into the large model for crosslingual retrieval, then the system can handle multilingual queries, but computational load increases

Engineering Contradiction:

Improvemultilingual query handlingVSAvoidcomputational load

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent performs translation of material knowledge points to standard language during offline database construction rather than during online query processing. This preliminary action shifts the computational burden to when computing resources are more readily available, reducing the computational load during actual query answering operations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the processing into offline preparation (translation and database construction) and online querying (matching and answer generation). This segmentation allows the computationally intensive translation task to be performed separately during offline hours, reducing the computational load during online operations

Inventive Principle:

Principle #1Segmentation

3Measurement precision

If character-level and semantic-level matching are performed to screen material knowledge points, then matching accuracy improves, but processing time increases

Engineering Contradiction:

Improvematching accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the matching process into two distinct levels: character-level matching for initial screening and semantic-level matching for fine-grained filtering. This segmentation allows the system to quickly eliminate irrelevant documents at the character level, then apply more computationally intensive semantic analysis only to a smaller subset of candidate documents, thereby maintaining high accuracy while reducing overall processing time

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies partial action by performing character-level matching on all material knowledge points first, then performing semantic-level matching only on the preliminarily screened results. This partial application of the more intensive semantic analysis to only necessary candidates maintains matching accuracy while significantly reducing processing time compared to applying semantic analysis to all documents

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260079983A1Retrieval-augmented query-and-answer method and apparatus, device, and medium

Publication Date: 2026.03.19 BEIJING BAIDU NETCOM SCI & TECH CO LTD

AI summary

A retrieval-augmented query-and-answer method includes acquiring a to-be-answered query; performing character-level matching and screening between the to-be-answered query and offline-stored material knowledge points in a database to obtain preliminarily screened material knowledge points; performing semantic-level matching and screening between the to-be-answered query and the preliminarily screened material knowledge points to obtain a finely screened material knowledge point; acquiring a bound material slice from the database based on the finely screened material knowledge point; generating prompt information based on the bound material slice and the to-be-answered query; and inputting the prompt information into a query-and-answer large model, processing the prompt information, and generating an answer. This solution can accelerate the query-and-answer processing speed based on a large model and improve the accuracy of crosslingual document retrieval.