Method for generating content of specific cultural background based on knowledge-enhanced reflection mechanism

Abstract

The method for generating content in specific cultural background based on knowledge-enhanced reflection mechanism, first, the cross-cultural task instruction and specific regional data are processed, the initial trajectory is generated in the generation space relying on the culture model, and the deviation of the output content in the cultural consistency is captured and judged by using the composite evaluator; a method combining mixed retrieval and cross coding reordering is used to filter and extract the data in the adaptive culture knowledge base as a highly matched core culture evidence set affected by the cultural context. Then, the trajectory with multi-source heterogeneous characteristics and the core culture evidence set are processed by using the self-reflection model of attention perception fusion, a strategy correction set is generated, the model evolution is fed back, and the text conforming to the target cultural values is obtained. The above method solves the problems of cultural flattening and illusion in the cross-cultural task of the large model, ensures the generation of high-quality text conforming to the local culture, and improves the self-alignment ability of the system.

Description

Technical Field

[0001] This invention addresses the challenges in natural language processing and artificial intelligence, where the cultural diversity of training data for large-scale language models leads to cultural flattening, cultural bias, and difficulty in aligning with specific regional values ​​in generated content across cross-cultural contexts. It proposes a method for generating content with specific cultural backgrounds based on a knowledge-enhanced reflection mechanism. Background Technology

[0002] With the rapid development of artificial intelligence and large-scale language models, the application of intelligent agents in text generation, dialogue interaction, and logical reasoning has undergone profound changes. While innovations in large-scale model technology have greatly improved information processing efficiency and generalization capabilities, they have also provided fertile ground for cultural biases and value misalignments in cross-cultural communication. Western dominance and cultural flattening in internal representations have become new characteristics of content generated by large-scale models, with phenomena such as ignoring the cultural customs of underrepresented regions and exporting stereotypes becoming increasingly prominent. These changes not only challenge the universality and credibility of traditional language models in different contexts but also place higher demands on the safety and ethical alignment of generative artificial intelligence. While promoting the global application of large-scale models, effectively achieving cross-cultural logical adaptation and value alignment has become a crucial issue that urgently needs to be addressed.

[0003] On the one hand, traditional model fine-tuning methods often lag behind the rapid changes in the global cultural context, making it difficult to adapt to dynamically changing cross-cultural characteristics in real time. This results in low cultural adaptation efficiency, high computational costs, and a lack of online self-evolution capabilities. On the other hand, the development of multi-agent collaboration and retrieval-enhanced generation has brought new opportunities and tools to large-scale model cultural alignment. By combining techniques such as proxy retrieval and linguistic reflection, agents can autonomously access external objective cultural knowledge bases during reasoning, identify their own potential cultural biases, and perform evidence-based dynamic self-correction and strategy evolution. Therefore, constructing an agent-based cultural alignment method that integrates knowledge retrieval and reflection mechanisms to prevent the risk of cultural conflicts in the output of large-scale models is particularly important. Summary of the Invention

[0004] To address the problems of existing technologies, this invention provides a method for generating content with a specific cultural context based on a knowledge-enhanced reflection mechanism. This method first processes cross-cultural task instructions and specific regional anchor data. Within the generation space, an initial trajectory containing reasoning chains and action sequences is generated based on a cultural model. A composite evaluator is then used to capture and determine deviations in the output content regarding factual accuracy and cultural consistency. A hybrid retrieval and cross-coding reordering method is employed to filter and extract a highly matched set of core cultural evidence influenced by the cultural context from a massive adaptive cultural knowledge base. Then, the processed trajectory with multi-source heterogeneous characteristics and the core cultural evidence set are processed using an attention-based self-reflection model to generate a strategy correction set, which feeds back into the model evolution, resulting in a final text change trend and generation result that conforms to the target cultural values ​​and logic.

[0005] To achieve the above objectives, the technical solution adopted by this invention is as follows: The method for generating content in a specific cultural context based on a knowledge-enhanced reflection mechanism involves the following steps: Step 1) Parse the input cross-cultural task instructions, construct an initial state space with specific cultural background anchors, and generate an initial output trajectory containing inference chains and specific operations through a cultural model; Step 2) Input the initial output trajectory into the composite evaluator, perform nonlinear mapping on multiple dimensions such as factuality, cultural consistency, and pragmatic suitability, construct a multi-dimensional evaluation feedback tensor, and determine whether to trigger the reflection mechanism. Step 3) When the reflection mechanism is triggered, a high-dimensional query vector is generated. Candidate fragments are recalled in the adaptive cultural knowledge base using a hybrid retrieval algorithm, and a cross-encoder is used to reorder the deep semantic interactions to obtain the Top-N core cultural evidence set. When the reflection mechanism is not triggered, it is determined that the initial output trajectory has met the target cultural alignment requirements, and the process jumps directly to step 6.

[0006] Step 4) Input the internal memory feature sequence and the core cultural evidence set retrieved from the outside into the self-reflection model, perform feature weighted fusion through the attention mechanism, and output reflection guidance text and structured strategy correction set; Step 5) Extract semantic features and evaluate confidence of the generated reflection guidance text and strategy correction set. Transform the reflection experiences that meet the high confidence threshold into structured topological nodes and incrementally update them into the adaptive cultural knowledge base to achieve the adaptive evolution of the knowledge base.

[0007] Step 6) Generate the final output text based on the processing path of the previous steps.

[0008] In step 1), the specific method is as follows: 1.1) Receive cross-cultural task instructions, extract cultural anchor features of the target region, and initialize the agent's current time step. state space This includes feature representations of the current task context and short-term episodic memory extraction.

[0009] 1.2) Convert the state space vector Input to cultural actor model In this model, the conditional probability distribution is based on autoregression. Sampling is performed to generate structured action tuples. The formula is as follows:

[0010] in, This represents high-level reasoning strategy planning; This represents a specific sequence of sub-actions unfolded according to time steps; This indicates the final text response generated, tailored to a specific culture. The parameter matrix represents the cultural actor model.

[0011] 1.3) Change the current state With the generated action tuple Combined, the multimodal interaction trajectory records are updated and generated. The formula is as follows:

[0012] in, Indicates the deadline for the time step. The complete set of generated trajectories.

[0013] In step 2), the specific method is as follows: 2.1) Record the generated trajectory Input to composite evaluator In the Composite Evaluator, the evaluator transforms the trajectory into a hidden layer representation tensor using a feature extraction operator.

[0014] 2.2) The composite estimator represents the tensor in the hidden layer. Multiple domain-specific discriminant functions are executed in parallel to output a multi-dimensional feedback scoring matrix. The formula is as follows:

[0015] in, Indicates the score for the accuracy of the facts; This represents the cultural consistency deviation penalty score calculated using cultural dimension mapping methods such as Hofstede VSM; Indicates the suitability of pragmatic register; Indicates the rate of blocking sensitive content; This indicates task-specific evaluation metrics (such as F1, EM, etc.).

[0016] 2.3) Calculate the weighted aggregation penalty function The formula for determining whether to trigger a knowledge-enhancing reflection loop is as follows:

[0017] in, Indicates the first The importance weight coefficients of each dimension, and satisfy the following: ; This represents the Sigmoid activation function, used to normalize the scores of each dimension to the [0,1] interval. If... If this occurs, the current output phase is interrupted to acquire external knowledge. The preset reflection trigger threshold is usually set to 0.35.

[0018] In step 3), the specific method is as follows: 3.1) Based on the feedback matrix Dimensions with scores below the threshold, combined with trajectory Using query generation functions Construct a search query string containing key cultural flaws .

[0019] 3.2) In an adaptive cultural knowledge base In this context, a hybrid retrieval mechanism is employed for query processing. With candidate documents Perform preliminary matching and scoring The formula is as follows:

[0020] in, This represents the sparse retrieval relevance score based on term frequency-inverse document frequency. and These represent the high-dimensional dense feature vectors of the query and the document after Sentence-BERT encoding, respectively. This represents a hyperparameter that controls the ratio of sparse to dense retrieval (the tuning range is usually between [0.3, 0.7]). This represents the L2 norm of a vector.

[0021] 3.3) Extract the top-N candidate documents with the highest initial retrieval scores and input them into the cross-encoder for fine-grained re-ranking calculation to capture deep interaction features.

[0022] ① The search query string With the extracted first Candidate documents (in By using special placeholders, a unified joint input sequence is constructed. The formula is as follows:

[0023] Subsequently, the joint sequence is mapped to a word embedding representation in a low-dimensional continuous space, and positional encoding is superimposed. Positional Encoding and Fragment Encoding (Segment Encoding) generates the initial feature matrix. .

[0024] ② Initial feature matrix Enter to include In the self-attention encoder network of layer 1, deep cross-attention features of the query and candidate documents are captured at the lexical and syntactic levels. For the 1st layer... The hidden state update formula for the layer encoder is as follows:

[0025] go through After deep layer interactions, mean pooling is performed on the token sequences corresponding to the query part and the document part respectively to extract the query dense feature vector containing global semantic information. With document dense feature vectors .

[0026] ③ Feature extraction includes concatenating the original vectors, absolute error distance (capturing semantic divergence), and Hadamard product (capturing feature co-occurrence), which are then fused into a high-dimensional interactive feature vector. The formula is as follows:

[0027] in, This represents the feature vector concatenation operation; This represents the element-wise multiplication operation; This indicates the absolute value operation.

[0028] ④ High-dimensional interactive feature vectors Feeding into multilayer perceptron networks ( This is used for nonlinear dimensionality reduction and feature extraction, followed by mapping through a linear classification layer and activation function to obtain the final fine-grained re-ranking relevance score. The formula is as follows:

[0029] in, and These represent the weight transpose matrix and bias term of the linear classification layer, respectively. This represents a non-linear activation function (such as Sigmoid) used to project unbounded correlation logits onto... probability interval

[0030] ⑤ After calculating the re-ranking scores of all candidate documents in the Top-N, based on The numerical values ​​are used to globally sort the document collection in descending order.

[0031] ⑥ Introduce a preset correlation confidence threshold. , extract the satisfying The former The highest-scoring document. These documents were identified as possessing high-quality background knowledge with deep semantic alignment, forming a set of external cultural evidence to guide subsequent generation. .

[0032] In step 4), the specific method is as follows: 4.1) Convert the trajectory tensor Feedback matrix and external cultural evidence collection As multi-source heterogeneous inputs, they are mapped to embedding sequences in the same feature space.

[0033] 4.2) In the self-reflection model The paper introduces an attention-based perceptual fusion mechanism to calculate the confidence fusion score of external evidence relative to internal parameter memory. .

[0034] ① Self-reflection model Extracting time steps from multi-round interaction trajectories The historical generation sequence is used to calculate the global hidden state feature vector of the model's short-term scenario buffer through a time-decay-based feature aggregation network. The formula is as follows:

[0035] in, Indicates at time step Single-step trajectory characteristics representation at time; The time decay factor that controls the rate at which historical information is forgotten; and These represent the encoded feature projection matrix and the bias term, respectively. The representation layer normalization operation is used to stabilize the numerical distribution of hidden states.

[0036] ②Collection of external cultural evidence The Middle Dense feature vectors of documents Features of internal hidden states Mapping to the same feature subspace. Utilizing a learnable bilinear mapping matrix. Calculate the unnormalized similarity scalar between the two. The formula is as follows:

[0037] in, Represents the transpose of the document's dense feature vector; This represents a cross-dimensional bilinear mapping parameter matrix used to capture the nonlinear interaction between the external evidence feature space and the internal memory feature space.

[0038] ③ Collection of external cultural evidence All of them The similarity scores of the candidate documents are used for global comparison. Exponential normalization is used to calculate the probability distribution of memory consistency similarity among documents from the perspective of internal memory. The formula is as follows:

[0039] in, Represents a set The first in One candidate document; This represents the temperature scaling hyperparameter, used to control the smoothness of the normalized probability distribution. When it is less than 1, it can significantly amplify the weight ratio of high-confidence documents.

[0040] ④ Using a small feedforward neural network, combined with the current query vector Uncertainty and internal memory Information entropy, dynamically calculating the bias weight of external evidence The formula is as follows:

[0041] in, This represents a gated sensor network; and For the mapping parameters of the linear output layer; Represents a non-linear activation function, ensuring the output weights Strictly bound to Within the closed interval. When the model determines that the confidence level of internal memory in the current cultural context is low, The value will adaptively increase to be more inclined to adopt external objective evidence.

[0042] ⑤ Self-reflection model The attention-aware fusion mechanism in the code reorders the scores of the cross-encoder using a weighted summation operator. Coupled with the normalized memory consistency similarity, the output is the first... Final confidence fusion score of external evidence The formula is as follows:

[0043] in, This indicates the objective semantic relevance of the externally retrieved evidence itself; the latter part... This indicates that it has passed. Normalized memory consistency similarity. This fusion score. This will then be used as an attention mask weight, broadcast to the features of the external evidence set to participate in the subsequent generation of reflective guidance text.

[0044] 4.3) Reflection Model Based on the weighted evidence distribution, autoregressive decoding is performed to output a structured sequence of reflection results. The formula is as follows:

[0045] in, This refers to natural language reflection guidance texts that explicitly cite external cultural facts; This indicates the parsed rule-level policy correction instructions (such as style replacement instructions or specific entity constraint avoidance instructions). This indicates an element-wise scaling operation that combines the score scalar with the embedded vector after broadcasting.

[0046] In step 5), the specific method is as follows: 5.1) Extracting Reflection Guidance Texts and correction instructions Using semantic compression networks Transform it into a low-dimensional, high-density semantic memory embedding. .

[0047] 5.2) Calculate the information entropy of the newly generated semantic memory. With unique N-gram proportion characteristics Establish a dynamic write confidence evaluation function The formula is as follows:

[0048] in, Indicates the probability of a word appearing in the vocabulary; This represents the number of unique N-grams in the set; Represents the total number of N-grams; and These are the scale penalty factors for information entropy and diversity characteristics, respectively.

[0049] 5.3) When the assessment confidence level meets the requirements At that time, the knowledge evolution writing protocol is activated, and new experiences are attached with cultural tags and timestamps, and are incrementally updated to the adaptive cultural knowledge base as new topology nodes, as shown in the following formula:

[0050] in, This represents the updated next-generation cultural knowledge base, thereby enabling the automatic evolution of the cultural knowledge graph from trial and error to task awareness and experience-driven approaches.

[0051] In step 6), the specific method is as follows: 6.1) If the reflection mechanism is not triggered, directly apply the Chinese actor model from step 1). The generated text response As the final output; 6.2) If the reflection mechanism is triggered, the reflection guidance text output in step 4) is used as a constraint to retrieve the updated adaptive cultural knowledge base in step 5) and drive the cultural actor model. Resample and generate content to output consistent text that aligns with the target cultural context.

[0052] The beneficial effects of this invention are as follows: This method for generating content with a specific cultural context based on a knowledge-enhanced reflection mechanism first processes user-input cross-cultural task instructions and anchor data from eight specific national cultural backgrounds. Within the generation space, an initial trajectory containing reasoning chains and action sequences is generated using a cultural model. A composite evaluator is then used to capture and determine deviations in the output content regarding factual accuracy and cultural consistency. A hybrid retrieval and cross-coding reordering approach is employed to filter and extract a highly matched set of core cultural evidence influenced by the cultural context from a massive adaptive cultural knowledge base. Then, the processed trajectory and cultural evidence, exhibiting multi-source heterogeneous characteristics, are processed using an attention-based perception fusion self-reflection model to generate a strategy correction set, which feeds back into the model's evolution, resulting in a final text change trend and generation outcome that aligns with the target cultural values ​​and logic.

[0053] This invention provides a method for generating content in specific cultural contexts based on a knowledge-enhanced reflection mechanism. It effectively solves the problems of cultural flattening, lack of factual basis in internal feedback leading to illusions, and high cost and difficulty in expansion of traditional fine-tuning in cross-cultural tasks of existing large-scale models. It ensures that large language models can stably and safely generate high-quality texts that conform to local cultural logic and norms in different national and regional contexts, and promotes the ability of artificial intelligence systems to autonomously align, self-correct, and continuously evolve in cross-cultural applications. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0055] A method for generating content in a specific cultural context based on knowledge-enhanced reflection mechanisms includes the following steps: 1) Parse the input cross-cultural task instructions, construct an initial state space with specific cultural background anchors, and generate an initial output trajectory containing inference chains and specific operations through a cultural model; (1) Receive cross-cultural task instructions, extract cultural anchor features of the target region, and initialize the agent's current time step. state space This includes feature representations of the current task context and short-term episodic memory extraction.

[0056] (2) Convert the state space vector Input to cultural actor model In this model, the conditional probability distribution is based on autoregression. Sampling is performed to generate structured action tuples. The formula is as follows:

[0057] in, This represents high-level reasoning strategy planning; This represents a specific sequence of sub-actions unfolded according to time steps; This indicates the final text response generated, tailored to a specific culture. The parameter matrix represents the cultural actor model.

[0058] (3) Change the current state With the generated action tuple Combined, the multimodal interaction trajectory records are updated and generated. The formula is as follows:

[0059] in, Indicates the deadline for the time step. The complete set of generated trajectories.

[0060] 2) Input the initial output trajectory into the composite evaluator, perform nonlinear mapping on multiple dimensions such as factuality, cultural consistency, and pragmatic suitability, construct a multi-dimensional evaluation feedback tensor, and determine whether to trigger the reflection mechanism; (1) Record the generated trajectory Input to composite evaluator In the Composite Evaluator, the evaluator transforms the trajectory into a hidden layer representation tensor using a feature extraction operator.

[0061] (2) The composite evaluator represents the tensor in the hidden layer. Multiple domain-specific discriminant functions are executed in parallel to output a multi-dimensional feedback scoring matrix. The formula is as follows:

[0062] in, Indicates the score for the accuracy of the facts; This represents the cultural consistency deviation penalty score calculated using cultural dimension mapping methods such as Hofstede VSM; Indicates the suitability of pragmatic register; Indicates the rate of blocking sensitive content; This indicates task-specific evaluation metrics (such as F1, EM, etc.).

[0063] (3) Calculate the weighted aggregation penalty function The formula for determining whether to trigger a knowledge-enhancing reflection loop is as follows:

[0064] in, Indicates the first The importance weight coefficients of each dimension, and satisfy the following: ; This represents the Sigmoid activation function, used to normalize the scores of each dimension to the [0,1] interval. If... If this occurs, the current output phase is interrupted to acquire external knowledge. The preset reflection trigger threshold is usually set to 0.35.

[0065] 3) When the reflection mechanism is triggered, a high-dimensional query vector is generated. Candidate fragments are recalled in the adaptive cultural knowledge base using a hybrid retrieval algorithm, and a cross-encoder is used to reorder them through deep semantic interaction to obtain the Top-N core cultural evidence set. When the reflection mechanism is not triggered, it is determined that the initial output trajectory has met the target cultural alignment requirements, and the process directly jumps to step 6). (1) Based on the feedback matrix Dimensions with scores below the threshold, combined with trajectory Using query generation functions Construct a search query string containing key cultural flaws .

[0066] (2) In the adaptive cultural knowledge base In this context, a hybrid retrieval mechanism is employed for query processing. With candidate documents Perform preliminary matching and scoring The formula is as follows:

[0067] in, This represents the sparse retrieval relevance score based on term frequency-inverse document frequency. and These represent the high-dimensional dense feature vectors of the query and the document after Sentence-BERT encoding, respectively. This represents a hyperparameter that controls the ratio of sparse to dense retrieval (the tuning range is usually between [0.3, 0.7]). This represents the L2 norm of a vector.

[0068] (3) Extract the top-N candidate documents with the highest preliminary search scores and input them into the cross-encoder for fine-grained re-ranking calculation to capture deep interaction features.

[0069] ① The search query string With the extracted first Candidate documents (in By using special placeholders, a unified joint input sequence is constructed. The formula is as follows:

[0070] Subsequently, the joint sequence is mapped to a word embedding representation in a low-dimensional continuous space, and positional encoding is superimposed. Positional Encoding and Fragment Encoding (Segment Encoding) generates the initial feature matrix. .

[0071] ② Initial feature matrix Enter to include In the self-attention encoder network of layer 1, deep cross-attention features of the query and candidate documents are captured at the lexical and syntactic levels. For the 1st layer... The hidden state update formula for the layer encoder is as follows:

[0072] go through After deep layer interactions, mean pooling is performed on the token sequences corresponding to the query part and the document part respectively to extract the query dense feature vector containing global semantic information. With document dense feature vectors .

[0073] ③ Feature extraction includes concatenating the original vectors, absolute error distance (capturing semantic divergence), and Hadamard product (capturing feature co-occurrence), which are then fused into a high-dimensional interactive feature vector. The formula is as follows:

[0074] in, This represents the feature vector concatenation operation; This represents the element-wise multiplication operation; This indicates the absolute value operation.

[0075] ④ High-dimensional interactive feature vectors Feeding into multilayer perceptron networks ( This is used for nonlinear dimensionality reduction and feature extraction, followed by mapping through a linear classification layer and activation function to obtain the final fine-grained re-ranking relevance score. The formula is as follows:

[0076] in, and These represent the weight transpose matrix and bias term of the linear classification layer, respectively. This represents a non-linear activation function (such as Sigmoid) used to project unbounded correlation logits onto... probability interval

[0077] ⑤ After calculating the re-ranking scores of all candidate documents in the Top-N, based on The numerical values ​​are used to globally sort the document collection in descending order.

[0078] ⑥ Introduce a preset correlation confidence threshold. , extract the satisfying The former The highest-scoring document. These documents were identified as possessing high-quality background knowledge with deep semantic alignment, forming a set of external cultural evidence to guide subsequent generation. .

[0079] 4) Input the internal memory feature sequence and the core cultural evidence set retrieved from the outside into the self-reflection model, perform feature weighted fusion through the attention mechanism, and output reflection guidance text and structured strategy correction set; (1) Convert the trajectory tensor Feedback matrix and external cultural evidence collection As multi-source heterogeneous inputs, they are mapped to embedding sequences in the same feature space.

[0080] (2) In the self-reflection model The paper introduces an attention-based perceptual fusion mechanism to calculate the confidence fusion score of external evidence relative to internal parameter memory. .

[0081] ① Self-reflection model Extracting time steps from multi-round interaction trajectories The historical generation sequence is used to calculate the global hidden state feature vector of the model's short-term scenario buffer through a time-decay-based feature aggregation network. The formula is as follows:

[0082] in, Indicates at time step Single-step trajectory characteristics representation at time; The time decay factor that controls the rate at which historical information is forgotten; and These represent the encoded feature projection matrix and the bias term, respectively. The representation layer normalization operation is used to stabilize the numerical distribution of hidden states.

[0083] ②Collection of external cultural evidence The Middle Dense feature vectors of documents Features of internal hidden states Mapping to the same feature subspace. Utilizing a learnable bilinear mapping matrix. Calculate the unnormalized similarity scalar between the two. The formula is as follows:

[0084] in, Represents the transpose of the document's dense feature vector; This represents a cross-dimensional bilinear mapping parameter matrix used to capture the nonlinear interaction between the external evidence feature space and the internal memory feature space.

[0085] ③ Collection of external cultural evidence All of them The similarity scores of the candidate documents are used for global comparison. Exponential normalization is used to calculate the probability distribution of memory consistency similarity among documents from the perspective of internal memory. The formula is as follows:

[0086] in, Represents a set The first in One candidate document; This represents the temperature scaling hyperparameter, used to control the smoothness of the normalized probability distribution. When it is less than 1, it can significantly amplify the weight ratio of high-confidence documents.

[0087] ④ Using a small feedforward neural network, combined with the current query vector Uncertainty and internal memory Information entropy, dynamically calculating the bias weight of external evidence The formula is as follows:

[0088] in, This represents a gated sensor network; and For the mapping parameters of the linear output layer; Represents a non-linear activation function, ensuring the output weights Strictly bound to Within the closed interval. When the model determines that the confidence level of internal memory in the current cultural context is low, The value will adaptively increase to be more inclined to adopt external objective evidence.

[0089] ⑤ Self-reflection model The attention-aware fusion mechanism in the code reorders the scores of the cross-encoder using a weighted summation operator. Coupled with the normalized memory consistency similarity, the output is the first... Final confidence fusion score of external evidence The formula is as follows:

[0090] in, This indicates the objective semantic relevance of the externally retrieved evidence itself; the latter part... This indicates that it has passed. Normalized memory consistency similarity. This fusion score. This will then be used as an attention mask weight, broadcast to the features of the external evidence set to participate in the subsequent generation of reflective guidance text.

[0091] (3) Reflection Model Based on the weighted evidence distribution, autoregressive decoding is performed to output a structured sequence of reflection results. The formula is as follows:

[0092] in, This refers to natural language reflection guidance texts that explicitly cite external cultural facts; This indicates the parsed rule-level policy correction instructions (such as style replacement instructions or specific entity constraint avoidance instructions). This indicates an element-wise scaling operation that combines the score scalar with the embedded vector after broadcasting.

[0093] 5) Extract semantic features and evaluate confidence of the generated reflection guidance text and strategy correction set. Transform the reflection experience that meets the high confidence threshold into structured topological nodes and incrementally update them into the adaptive cultural knowledge base to achieve adaptive evolution of the knowledge base.

[0094] (1) Extracting reflection guidance texts and correction instructions Using semantic compression networks Transform it into a low-dimensional, high-density semantic memory embedding. .

[0095] (2) Calculate the information entropy of the newly generated semantic memory With unique N-gram proportion characteristics Establish a dynamic write confidence evaluation function The formula is as follows:

[0096] in, Indicates the probability of a word appearing in the vocabulary; This represents the number of unique N-grams in the set; Represents the total number of N-grams; and These are the scale penalty factors for information entropy and diversity characteristics, respectively.

[0097] (3) When the assessment confidence level meets At that time, the knowledge evolution writing protocol is activated, and new experiences are attached with cultural tags and timestamps, and are incrementally updated to the adaptive cultural knowledge base as new topology nodes, as shown in the following formula:

[0098] in, This represents the updated next-generation cultural knowledge base, thereby enabling the automatic evolution of the cultural knowledge graph from trial and error to task awareness and experience-driven approaches.

[0099] 6) Generate the final output text based on the processing path of the previous steps.

[0100] (1) If the reflection mechanism is not triggered, directly translate the Chinese actor model in step 1). The generated text response As the final output; (2) If the reflection mechanism is triggered, the reflection guidance text output in step 4) is used as a constraint to retrieve the updated adaptive cultural knowledge base in step 5) and drive the cultural actor model. Resample and generate content to output consistent text that aligns with the target cultural context.

Claims

1. A method for generating content in a specific cultural context based on a knowledge-enhanced reflection mechanism, characterized in that: The steps are as follows: Step 1) Parse the input cross-cultural task instructions, construct an initial state space with specific cultural background anchors, and generate an initial output trajectory containing inference chains and specific operations through a cultural model; Step 2) Input the initial output trajectory into the composite evaluator, perform nonlinear mapping on multiple dimensions such as factuality, cultural consistency, and pragmatic suitability, construct a multi-dimensional evaluation feedback tensor, and determine whether to trigger the reflection mechanism. Step 3) When the reflection mechanism is triggered, a high-dimensional query vector is generated. Candidate fragments are recalled in the adaptive cultural knowledge base using a hybrid retrieval algorithm. A cross-encoder is used to reorder the deep semantic interactions to obtain the Top-N core cultural evidence set. When the reflection mechanism is not triggered, it is determined that the initial output trajectory has met the target cultural alignment requirements, and the process jumps to step 6. Step 4) Input the internal memory feature sequence and the core cultural evidence set retrieved from the outside into the self-reflection model, perform feature weighted fusion through the attention mechanism, and output reflection guidance text and structured strategy correction set; Step 5) Extract semantic features and evaluate confidence of the generated reflection guidance text and strategy correction set. Transform the reflection experiences that meet the high confidence threshold into structured topological nodes and incrementally update them into the adaptive cultural knowledge base to achieve the adaptive evolution of the knowledge base. Step 6) Generate the final output text based on the processing path of the previous steps.

2. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 1), the specific method is as follows: Step 1.1) Receive the cross-cultural task instruction, extract the cultural anchor features of the target region, and initialize the agent's current time step. state space This includes feature representations extracted from the current task context and short-term episodic memory; Step 1.2) Convert the state space vector Input to cultural actor model In this model, the conditional probability distribution is based on autoregression. Sampling is performed to generate structured action tuples. The formula is as follows: in, This represents high-level reasoning strategy planning; This represents a specific sequence of sub-actions unfolded according to time steps; This indicates the final text response generated, tailored to a specific culture. The parameter matrix representing the cultural actor model; Step 1.3) Set the current state With the generated action tuple Combined, the multimodal interaction trajectory records are updated and generated. The formula is as follows: in, Indicates the deadline for the time step. The complete set of generated trajectories.

3. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 2), the specific method is as follows: Step 2.1) Record the generated trajectory Input to composite evaluator In the process, the evaluator transforms the trajectory into a hidden layer representation tensor through a feature extraction operator. Step 2.2) The composite estimator represents the tensor in the hidden layer. Multiple domain-specific discriminant functions are executed in parallel to output a multi-dimensional feedback scoring matrix. The formula is as follows: in, Indicates the score for the accuracy of the facts; This represents the cultural consistency deviation penalty score calculated using cultural dimension mapping methods such as Hofstede VSM; Indicates the suitability of pragmatic register; Indicates the rate of blocking sensitive content; Indicates task-specific evaluation metrics; Step 2.3) Calculate the weighted aggregation penalty function. The formula for determining whether to trigger a knowledge-enhancing reflection loop is as follows: in, Indicates the first The importance weight coefficients of each dimension, and satisfy the following: ; This represents the Sigmoid activation function, used to normalize the scores of each dimension to the [0,1] interval; if If this occurs, the current output phase is interrupted to acquire external knowledge. This is the preset reflection trigger threshold.

4. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 3), the specific method is as follows: Step 3.1) Based on the feedback matrix Dimensions with scores below the threshold, combined with trajectory Using query generation functions Construct a search query string containing key cultural flaws ; Step 3.2) In the adaptive cultural knowledge base In this context, a hybrid retrieval mechanism is employed for the query. With candidate documents Perform preliminary matching and scoring The formula is as follows: in, This represents the sparse retrieval relevance score based on term frequency-inverse document frequency. and These represent the high-dimensional dense feature vectors of the query and the document after Sentence-BERT encoding, respectively. This represents a hyperparameter that controls the ratio of sparse to dense retrieval. Describes the L2 norm of a vector; Step 3.3) Extract the Top-N candidate documents with the highest preliminary retrieval scores, input them into the cross-encoder for fine-grained re-ranking calculation, and capture deep interaction features; Step 3.3.1) Retrieve the query string With the extracted first Candidate documents (in By using special placeholders, a unified joint input sequence is constructed. The formula is as follows: Subsequently, the joint sequence is mapped to a word embedding representation in a low-dimensional continuous space, and positional encoding is superimposed. With fragment encoding Generate the initial feature matrix ; Step 3.3.2) Initialize the feature matrix Enter to include In the layer-1 self-attention encoder network, deep cross-attention features of the query and candidate documents at the lexical and syntactic levels are captured. The hidden state update formula for the layer encoder is as follows: go through After deep layer interactions, average pooling is performed on the token sequences corresponding to the query part and the document part respectively to extract the query dense feature vector containing global semantic information. With document dense feature vectors ; Step 3.3.3) Extracting features includes concatenating the original vectors, absolute error distance, and Hadamard product, and fusing them into a high-dimensional interactive feature vector. The formula is as follows: in, This represents the feature vector concatenation operation; This represents the element-wise multiplication operation; This represents the absolute value operation; Step 3.3.4) The high-dimensional interactive feature vector The data is fed into a multilayer perceptron network for nonlinear dimensionality reduction and feature extraction, and then mapped to a final fine-grained reordering relevance score through a linear classification layer and activation function. The formula is as follows: in, and These represent the weight transpose matrix and bias term of the linear classification layer, respectively. This represents a non-linear activation function used to project unbounded correlation logits onto... The probability range; Step 3.3.5) After calculating the re-ranking scores of all candidate documents in the Top-N, based on... The numerical values ​​are used to globally sort the document collection in descending order. Step 3.3.6) Introduce a preset relevance confidence threshold. , extract the satisfying The former The highest-scoring document, this These documents were identified as possessing high-quality background knowledge with deep semantic alignment, forming a set of external cultural evidence to guide subsequent generation. .

5. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 4), the specific method is as follows: Step 4.1) Convert the trajectory tensor Feedback matrix and external cultural evidence collection As a multi-source heterogeneous input, it is mapped to an embedding sequence in the same feature space; Step 4.2) In the self-reflection model The paper introduces an attention-based perceptual fusion mechanism to calculate the confidence fusion score of external evidence relative to internal parameter memory. ; Step 4.2.1) Self-reflection model Extracting time steps from multi-round interaction trajectories The historical generation sequence is used to calculate the global hidden state feature vector of the model's short-term scenario buffer through a time-decay-based feature aggregation network. The formula is as follows: in, Indicates at time step Single-step trajectory characteristics representation at time; The time decay factor that controls the rate at which historical information is forgotten; and These represent the encoded feature projection matrix and the bias term, respectively. The representation layer normalization operation is used to stabilize the numerical distribution of hidden states; Step 4.2.2) Set up external cultural evidence The Middle Dense feature vectors of documents Features of internal hidden states Mapping to the same feature subspace; utilizing a learnable bilinear mapping matrix. Calculate the unnormalized similarity scalar between the two. The formula is as follows: in, Represents the transpose of the document's dense feature vector; This represents the cross-dimensional bilinear mapping parameter matrix, used to capture the nonlinear interaction between the external evidence feature space and the internal memory feature space; Step 4.2.3) Set of external cultural evidence All of them The similarity scores of the candidate documents are used for global comparison. Exponential normalization is used to calculate the probability distribution of memory consistency similarity among documents from the perspective of internal memory. The formula is as follows: in, Represents a set The first in One candidate document; This represents the temperature scaling hyperparameter, used to control the smoothness of the normalized probability distribution. When it is less than 1, it can significantly amplify the weight ratio of high-confidence documents. Step 4.2.4) Using a small feedforward neural network, combined with the current query vector Uncertainty and internal memory Information entropy, dynamically calculating the bias weight of external evidence The formula is as follows: in, This represents a gated sensor network; and For the mapping parameters of the linear output layer; Represents a non-linear activation function, ensuring the output weights Strictly bound to Within the closed interval; when the model determines that the confidence level of internal memory in the current cultural context is low, The value will adaptively increase to be more inclined to accept external objective evidence; Step 4.2.5) Self-reflection model The attention-aware fusion mechanism in the code reorders the scores of the cross-encoder using a weighted summation operator. Coupled with the normalized memory consistency similarity, the output is the first... Final confidence fusion score of external evidence The formula is as follows: in, This indicates the objective semantic relevance of the externally retrieved evidence itself; the latter part... This indicates that it has passed. Normalized memory consistency similarity; the fusion score This will then be used as an attention mask weight and broadcast to the features of the external evidence set to participate in the subsequent generation of reflection guidance text; Step 4.3) Reflection Model Based on the weighted evidence distribution, autoregressive decoding is performed to output a structured sequence of reflection results. The formula is as follows: in, This refers to natural language reflection guidance texts that explicitly cite external cultural facts; This represents the parsed rule-level policy correction instruction; This indicates an element-wise scaling operation that combines the score scalar with the embedded vector after broadcasting.

6. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 5), the specific method is as follows: Step 5.1) Extract the reflection guidance text and strategy correction instructions Using semantic compression networks Transform it into a low-dimensional, high-density semantic memory embedding. ; Step 5.2) Calculate the information entropy of the newly generated semantic memory. With unique N-gram proportion characteristics Establish a dynamic write confidence evaluation function The formula is as follows: in, Indicates the probability of a word appearing in the vocabulary; This represents the number of unique N-grams in the set; Represents the total number of N-grams; and These are the scale penalty factors for information entropy and diversity characteristics, respectively; Step 5.3) When the assessment confidence level meets the requirements At that time, the knowledge evolution writing protocol is activated, and new experiences are attached with cultural tags and timestamps, and are incrementally updated to the adaptive cultural knowledge base as new topology nodes, as shown in the following formula: in, This represents the updated next-generation cultural knowledge base, thereby enabling the automatic evolution of the cultural knowledge graph from trial and error to task awareness and experience-driven approaches.

7. The method for generating specific cultural background content based on a knowledge-enhanced reflection mechanism according to claim 1, characterized in that, In step 6), the specific method is as follows: Step 6.1) If the reflection mechanism is not triggered, directly translate the Chinese actor model from Step 1). The generated text response As the final output; Step 6.2) If the reflection mechanism is triggered, the reflection guidance text output in Step 4) is used as a constraint to retrieve the updated adaptive cultural knowledge base in Step 5) and drive the cultural actor model. Resample and generate content to output consistent text that aligns with the target cultural context.

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