Intelligent translation calibration method based on RAG knowledge base

Abstract

The invention discloses an intelligent translation calibration method based on an RAG knowledge base, and belongs to the technical field of artificial intelligence and natural language processing. In order to solve the problems that an existing large language model (LLM) is insufficient in professional term translation accuracy in text translation, low in RAG system retrieval efficiency and the like, an intelligent calibration triggering mechanism is constructed, so that the LLM can autonomously judge whether translation content needs knowledge base enhancement or not in the modes of field detection, term density analysis, user-defined triggering and the like; a full-amount retrieval strategy is changed, and the system efficiency is improved; an RAG knowledge base retrieval framework based on function calling is constructed, multiple types of knowledge sources such as a translation memory library, a term table and a professional corpus are integrated, and accurate domain knowledge is provided for translation calibration; flexible multi-knowledge-base configuration and priority management are supported, and a user can select knowledge base types and set retrieval strategies and fusion weights according to needs; through a progressive translation optimization process of initial translation, intelligent judgment, knowledge base retrieval and calibration optimization, accurate calibration of different granularities such as term level, sentence level and paragraph level is realized, and a standardized knowledge base interface specification is provided. According to the method, the defects of LLM in the aspects of translation accuracy and knowledge base utilization efficiency in the professional field are effectively overcome, and the accuracy and efficiency of knowledge-intensive translation tasks are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial intelligence and natural language processing, and particularly relates to an intelligent translation calibration method based on a RAG knowledge base. The purpose is to solve the problem of insufficient translation accuracy of professional terms, domain-specific expressions and knowledge-intensive content in the text translation of existing large language models (LLM) by constructing an intelligent RAG calibration mechanism, thereby improving the performance and efficiency of LLM in translation tasks. BACKGROUND

[0002] Currently, in the field of artificial intelligence and natural language processing, machine translation related technologies continue to develop. Large language model (LLM) translation technology is based on the Transformer architecture, pre-trained through large-scale corpus, and has better performance than traditional neural machine translation (NMT) in machine translation tasks. By using prompt engineering to guide the model to complete translation, it can effectively handle long sentences and long documents, such as translating sentences of about 80 words or even 512-word documents. However, "Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models" (ACL2025) points out that there are challenges in professional field translation and rare word prediction, and "Do LLMs Understand Your Translations? Evaluating Paragraph-level MT with Question Answering" (arXiv:2504.07583, 2024) also finds that it lacks the ability to maintain cross-sentence boundary meaning in paragraph-level translation. Slator's 2024 industry survey shows that 72.7% of professionals believe that translation quality evaluation still needs further research, and translation quality problems need to be solved urgently.

[0003] In the application of retrieval augmented generation (RAG) technology, although some studies have confirmed its role in improving translation quality (such as "Retrieval-Augmented Machine Translation with Unstructured Knowledge" (Wang et al., arXiv:2412.04342, 2024) which achieved a BLEU score improvement of 1.58-3.09 in 79K samples), but the existing system generally adopts the "full retrieval" strategy, which performs external knowledge retrieval on all content, resulting in low efficiency and insufficient pertinence. Although the open source project T-Ragx optimizes translation through translation memory and terminology table, it does not solve the problem of intelligent triggering of retrieval.

[0004] Function call technology has been introduced by mainstream LLM vendors since July 2023, supporting dynamic invocation of external tools (such as dictionaries, translation APIs) by models, but its application in the field of translation is mostly limited to simple tool integration, lacking a systematic framework for real-time calibration of translation quality. Traditional computer-aided translation (CAT) tools (such as Trados Studio, MemoQ) rely on static translation memory libraries, have limited ability to handle dynamic content and emerging terminology, and lack deep integration with LLM RAG mechanisms.

[0005] In addition, the prior art has deficiencies in professional domain knowledge base integration, standardized interface design, etc.: the consistency of professional term translation is difficult to guarantee, the knowledge base format depends on a specific system, and users cannot flexibly configure multiple sources of knowledge, resulting in translation quality and efficiency that cannot meet the needs of diverse scenarios.

[0006] Therefore, it is necessary to provide an intelligent translation calibration method based on RAG knowledge base to solve the above technical problems. SUMMARY

[0007] The present application focuses on the field of artificial intelligence and natural language processing, aiming to solve the problems of insufficient translation accuracy in professional fields and low utilization efficiency of knowledge bases in existing large language model (LLM) translation systems. An intelligent translation calibration method based on RAG knowledge base is designed to comprehensively improve the performance of LLM in translation tasks. The core of the present application is to build a complete and innovative solution, adding an intelligent calibration layer to the traditional LLM translation, dynamically retrieving professional knowledge bases through a function call mechanism, and realizing intelligent optimization of translation quality. The specific invention contents are as follows: Step S1, establish an intelligent calibration trigger mechanism to realize on-demand retrieval, significantly improving system efficiency; Step S2, build a RAG knowledge base retrieval framework based on function calls, to provide accurate domain knowledge support for translation calibration; Step S3, based on multi-knowledge base configuration and priority management, support users to select and enable translation memory libraries, terminology tables, domain corpus, etc. through the system management interface according to the translation field and quality requirements, customize the retrieval range, depth, etc. of each knowledge base, and adjust the fusion weight; Step S4, establish a progressive translation optimization process, through the complete closed loop of "initial translation → intelligent judgment → knowledge base retrieval → calibration optimization", input the text to be translated into LLM supporting function calls to generate initial translation results; analyze the original text through the intelligent calibration trigger mechanism to determine whether retrieval is needed; if retrieval is triggered, obtain knowledge based on the above RAG knowledge base retrieval framework; generate calibration prompts based on the retrieved knowledge to guide LLM to optimize the initial translation results to output the final translation; Step S5, support multiple calibration granularity and knowledge base standards to meet the translation needs of different professional fields.

[0008] Preferably, the intelligent calibration trigger mechanism in step S1 includes the following steps: Step S1.1, support users to add terms according to professional needs by constructing extensible field term dictionary. Use regular expressions and semantic matching algorithms to identify and classify professional terms in input text; Step S1.2, use the semantic understanding ability of LLM to analyze the text theme and compare with the pre-built knowledge graph of each professional field to determine the professional field to which the text belongs; Step S1.3, extract the format identification, header metadata, content label and other information of the document, and automatically identify and classify the document type (such as patent specification, academic paper, business contract, etc.) according to the preset rules; Step S1.4, count the frequency of professional terms (number of terms / total number of words in text), and evaluate the context complexity (number of long sentences, proportion of complex sentence patterns, etc.) combined with syntax and semantic dependency analysis. For different fields, set dynamic threshold values. When the term frequency exceeds the threshold and the context complexity meets the requirements, trigger knowledge base retrieval; Step S1.5, provide a visual interface to support users to configure trigger rules: set specific field keywords, automatically trigger calibration when the translation content contains the keywords; set mandatory calibration rules for medical, legal and other fields; automatically associate corresponding knowledge bases according to document fields, and support users to customize the association rules between fields and knowledge bases.

[0009] Preferably, the construction of RAG knowledge base retrieval framework based on function call in step S2 includes the following sub-steps: Step S2.1, standardize the function interface, and receive the query text, professional field identification, language pair configuration and knowledge base configuration information containing priority and weight settings as input parameters through the RAG knowledge base retrieval function. Output classified retrieval results and corresponding confidence scores; Step S2.2, the core processing flow of RAG knowledge base retrieval framework based on function call.

[0010] Preferably, in step S2.2, the core processing flow of the application specifically includes: Step S2.2.1, vectorize the query text and consider professional field features in the vectorization process to ensure the accuracy of semantic similarity calculation; Step S2.2.2, starting multi-knowledge base parallel retrieval: when the translation memory base is enabled, the historical translation records are retrieved in the specified language pair and field range through vector similarity matching technology; for the enabled terminology table knowledge base, the strategy of combining exact matching and fuzzy matching is adopted to obtain the standard terminology translation pair; semantic retrieval is performed in the field corpus to extract relevant context information and translation references; Step S2.2.3, based on the user preset priority strategy, the multi-knowledge base retrieval results are intelligently fused and relevance sorted through the result fusion and sorting algorithm to generate the final retrieval result set.

[0011] Preferably, in step S5, the present application supports multiple calibration granularities and knowledge base standards, which specifically include: Step S5.1, supporting multi-granularity accurate calibration at different levels such as term level, sentence level, paragraph level, etc. Step S5.2, defining standardized knowledge base interface specifications.

[0012] Preferably, in step S5.1, the accurate calibration of multiple calibration granularities is as follows: Step S5.1.1, term-level calibration for accurate translation of specific professional terms; Step S5.1.2, sentence-level calibration to optimize the translation quality and fluency of the entire sentence; Step S5.1.3, paragraph-level calibration to maintain the overall consistency and coherence of the paragraph content.

[0013] Preferably, in step S5.2, the standardized knowledge base standards specifically include: Step S5.2.1, initializing parameters (distinguishing knowledge base types, specifying the professional field to which it belongs, defining supported language pairs) and core function interfaces (standard retrieval interface, metadata acquisition interface, data format verification interface) to define standardized knowledge base interface specifications; Step S5.2.2, stipulating that knowledge base data is stored in a structured format, each data entry contains source text, target text, field identification, confidence, timestamp, metadata, etc. Core fields ensure the uniformity and interoperability of different source knowledge bases and meet the translation needs of different professional fields.

[0014] Compared with the prior art, the present application has the beneficial effects that: through the multi-dimensional calibration judgment mechanism, intelligent on-demand calibration is realized, combined with field feature detection technology, waste of full retrieval is avoided, efficiency is improved, and multi-granularity calibration ensures accuracy; at the same time, the function calling technology is deeply applied to RAG translation calibration, multiple types of knowledge sources are integrated, and the quality of professional document translation is significantly improved, providing users with high-quality and professional translation services. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 : Overall architecture diagram of intelligent translation calibration system based on RAG knowledge base.

[0016] Figure 2 : Detailed flow chart of intelligent translation calibration based on RAG knowledge base.

[0017] Figure 3 : Decision diagram of intelligent calibration judgment mechanism.

[0018] Figure 4 : Architecture diagram of multi-knowledge base retrieval and fusion.

[0019] Figure 5 : Flow chart of translation optimization algorithm. DETAILED DESCRIPTION

[0020] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather the embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only and are not intended to limit the scope of protection of the present disclosure.

[0021] In the embodiments of the present disclosure, at the hardware level, a high-performance server cluster is adopted, mainstream LLM supporting function calls is selected for software, and a vector database is deployed to store knowledge source data. As shown in Figure 1 When translating, the LLM first performs preliminary translation on the input text; the intelligent judgment module judges whether to trigger calibration from three dimensions of domain feature detection, term density analysis, and user configuration triggering, such as judging the domain to which the text belongs through term recognition and semantic analysis, setting a threshold value by counting the frequency of terms and combining the complexity of the context, or triggering retrieval according to user-defined keywords, rules, and the like. If triggered, the multi-knowledge base retrieval module receives parameters through a standardized function interface, vectorizes the query text, and then retrieves translation memory, term table, domain corpus, and the like in parallel, the knowledge fusion module calculates the score and sorts the retrieval results according to the multi-dimensional scoring mechanism, and outputs the Top-K results.

[0022] In the embodiments of the present disclosure, as shown in Figure 2As shown, through the three dimensions of field feature detection, term density analysis and user configuration triggering, intelligent calibration judgment is cooperatively performed, and then through multi-dimensional analysis, it is determined whether knowledge base retrieval calibration is needed. The implementation mode of the multi-knowledge base parallel retrieval mechanism is: when the knowledge base configuration enables the translation memory base, the system uses the vector similarity matching technology to retrieve the historical translation records in the specified language pair and field range; for the enabled term table knowledge base, the system adopts the retrieval strategy combining the exact matching and fuzzy matching to obtain the translation contrast of the standard term; and in the field corpus retrieval, the system performs semantic retrieval to extract the related context information and translation reference.

[0023] In the embodiment of the application, as shown in Figure 3 As shown in the field detection algorithm, an extensible field term dictionary is constructed, regular expressions and semantic matching algorithms are used to identify and classify professional terms in the input text, and the specific steps are as follows: The semantic understanding ability of the LLM is used to analyze the text theme, and is compared with the pre-constructed knowledge graph to determine the professional field to which it belongs; at the same time, by extracting document format identifiers, metadata and other information, the document type is automatically identified according to the preset rules; In the term density analysis, the frequency of professional terms in the text is counted, and when the frequency is above the threshold and the context complexity such as term collocation and sentence structure is considered comprehensively, the knowledge base retrieval is triggered; In addition, the user can set specific field keywords through the system configuration interface to realize keyword triggering calibration; specific field forced calibration rules are set to execute field rule triggering; and the association rules of the field and the knowledge base can also be customized to realize the knowledge base association triggering according to the automatic matching of the document field and the associated knowledge base, so as to meet the diversified translation scene needs.

[0024] In the embodiment of the application, as shown in Figure 4 As shown in the RAG knowledge base retrieval framework implementation, the multi-knowledge base retrieval function follows a specific interface specification, receives the query text to be retrieved, the professional field identifier (supports user-defined classification), the language pair configuration (supports any combination) and the knowledge base configuration information containing priority and weight settings as input, and outputs the classification retrieval result and the corresponding confidence score. The core processing flow is as follows: Step S1, first, the query text is vectorized, the input text is converted into a high-dimensional vector representation, and in the vectorization process, the professional field characteristics are fully considered to ensure the accuracy of the semantic similarity calculation; Step S2, then start the multi-knowledge base parallel retrieval mechanism, when the knowledge base configuration enables the translation memory base, the system uses the vector similarity matching technology to retrieve the historical translation record in the specified language pair and domain range; for the enabled term table knowledge base, the retrieval strategy combining accurate matching and fuzzy matching is executed to obtain the standard term translation pair; semantic retrieval is performed in the domain corpus to extract relevant context information and translation reference; Step S3, finally through the result fusion and sorting algorithm, the system intelligently fuses and sorts the retrieval results of multiple knowledge bases according to the user's preset priority strategy to generate the final retrieval result set. Specifically, the following substeps are included: A multi-dimensional scoring mechanism is used to quantitatively evaluate the retrieval results from different sources. The translation memory base score is based on the semantic similarity calculation base score, combined with the historical translation quality, usage frequency and user-set weight coefficient; the term table score is evaluated by accurate matching and context matching, considering the term authority, domain relevance and applying priority weight adjustment; the corpus score calculates the semantic relevance and context matching degree, evaluates the timeliness of the corpus, the domain specialty and combines the corpus weight adjustment; Then, through intelligent sorting and screening strategy, all scoring results are uniformly arranged in descending order to ensure that high-quality results are displayed first, and according to the user-set result quantity limit, the top-K most relevant retrieval results are returned to balance the retrieval efficiency and result quality.

[0025] In the embodiment of the application, as shown in Figure 5 In the translation optimization link, the system generates prompt content according to the structured calibration prompt template based on the retrieval information. In the template, the context information part extracts the original text and the preliminary translation result of the large language model to provide a basis for optimization; Further, the reference knowledge part integrates the standard translation method in the professional term table, the similar bilingual fragment matching result of the translation memory base, and the sentence pattern and expression habit reference in the domain corpus; the optimization instruction part specifies the calibration target granularity at the term level, sentence level or paragraph level according to the translation demand, and determines the optimization focus, such as term accuracy, sentence fluency or paragraph logical coherence; At the same time, the intelligent prompt generation algorithm dynamically adjusts the prompt content according to the retrieval knowledge type and confidence. When high-confidence term matching results are retrieved, the term calibration is emphasized; when relevant translation memories are obtained, the consistency optimization of the overall expression is highlighted. After receiving the calibration prompt, the large language model performs multi-granularity calibration: term-level calibration forces to replace incorrect or non-standard terms to ensure the consistency of professional vocabulary; sentence-level calibration optimizes the sentence structure to make the translation conform to the domain expression habit; paragraph-level calibration adjusts the inter-sentence logical relationship to ensure the coherence of the paragraph semantics, and finally outputs high-quality translation.

Claims

1. An intelligent translation calibration method based on RAG knowledge base, characterized in that: include: A. Establish an intelligent calibration trigger mechanism. This uses a domain feature detection algorithm to analyze text terminology, semantics, and document type. It then uses a term density analysis algorithm to calculate term frequency and compare it with a threshold. Furthermore, based on user-configured keywords, domain rules, or knowledge base association rules, the large language model can autonomously determine whether the translated content requires knowledge base enhancement. B. Build a function-based RAG knowledge base retrieval framework and design a standardized knowledge base interface. This framework receives query text, domain identifiers, language pair configuration, and knowledge base configuration information through function calls. It then performs parallel searches on translation memories, domain glossaries, and domain corpora, and employs intelligent fusion algorithms to fuse and rank the search results. C. Flexible multi-knowledge base configuration and priority management. Users can select the type of knowledge base to enable and set the search strategy and fusion weight. D. Establish a progressive translation optimization process. The large language model performs an initial translation of the input text. After intelligent judgment triggers a knowledge base search, calibration prompts are generated based on the integrated search results to guide the large language model to perform calibration optimization, achieving precise calibration at different granularities such as term level, sentence level, and paragraph level.

2. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The domain feature detection specifically includes: A. Use regular expressions and semantic matching algorithms to identify professional terms in text; B. Use a large language model for semantic understanding and compare it with pre-built knowledge graphs in various professional fields to determine the professional field to which the text belongs; C. Extract the document’s format identifier, header metadata, content tags and other information, and identify the document type based on preset rules.

3. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The term density analysis is specifically as follows: the frequency of occurrence of professional terms in the text is counted and divided by the total number of words in the text to obtain the term frequency; combined with the context complexity evaluated based on syntactic and semantic dependency analysis, it is compared with a preset threshold. When the term frequency exceeds the threshold and the context complexity reaches a certain level, a knowledge base search is triggered.

4. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The user configuration trigger includes: the user sets specific field keywords in the system configuration interface, and calibration is automatically triggered when the translation content contains keywords; the user sets mandatory calibration rules for specific fields, and performs knowledge base retrieval and calibration when the translation content belongs to this field; the preset knowledge base is automatically associated according to the field to which the document belongs, and supports user-defined association rules between fields and knowledge bases.

5. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The calibration prompt generation dynamically generates structured prompts containing source text, initial translation results, multi-source reference knowledge, and clear calibration target granularity and focus based on the retrieved knowledge type and confidence.

6. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The function call-based parallel retrieval from multiple knowledge bases specifically includes: defining a standardized function interface, receiving the query text to be retrieved, professional field identifier, language pair configuration and knowledge base configuration information; vectorizing the query text; using vector similarity matching technology to retrieve historical translation records in the translation memory; using a combination of exact matching and fuzzy matching to retrieve professional terminology; and performing semantic retrieval in the domain corpus.

7. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: The context information part of the calibration prompt includes the original text and the initial translation result, the reference knowledge part integrates professional terminology comparison, translation memory matching results, and domain corpus reference, and the optimization instruction part clearly specifies the target granularity and optimization focus of the calibration.

8. The intelligent translation calibration method based on the RAG knowledge base according to claim 1, characterized in that: Also includes: Define a standardized knowledge base interface specification, which includes initialization parameters and core functional interfaces. The initialization parameters are used to clarify the knowledge base type, professional field and supported language pairs. The core functional interfaces include standard retrieval interface, metadata acquisition interface and data format verification interface.

9. The intelligent translation calibration method based on the RAG knowledge base according to any one of claims 1 to 8, characterized in that: The system provides configurable parameters such as calibration threshold, knowledge source weight, search scope and output format to meet the personalized translation needs of different users.

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