Large language model dialogue method, large language model construction method, device and equipment

Abstract

The invention discloses a big language model dialogue method, a big language model construction method, a big language model dialogue device, a big language model dialogue system and big language model construction equipment. According to the big language model dialogue method, a deep thinking part of a model is trained through an inference type big language model, so that the deep thinking part can carry out detailed inference analysis on risk problems, and the inference analysis comprises analysis of user intentions and strategy selection. And analyzing the user problem to analyze a risk type, a user risk level, a risk intention and a demand intention in the user problem. According to the requirements and the safety criteria of the user, a safety reply strategy and a requirement meeting strategy are selected, the safety reply strategy comprises refusal or forward guidance of the risk intention, and the requirement meeting strategy comprises meeting of the requirements of the user within a legal range. By adopting the processing mode, the problem of excessive rejection of the large language model can be effectively solved, and risk migration caused by rejection type interaction is avoided.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a large language model dialogue method and apparatus, a large language model construction method and apparatus, a large language model processing system, and electronic devices. Background Technology

[0002] Large Language Models (LLMs) are a deep learning-based artificial intelligence technique. Their core principle is to train models using large-scale datasets, enabling them to generate natural language text or understand the meaning of spoken text, achieving near-human-level text generation capabilities. Compared to traditional Natural Language Processing (NLP) models, LLMs can better understand and generate natural text, while also exhibiting a degree of logical thinking and reasoning ability.

[0003] Security mechanisms for large language models are a hot research topic in the field. Current security mechanisms for large language models generally adopt a "defensive denial" strategy, typically blocking risky requests with standard phrases such as "I cannot assist with this type of request." While this approach can mitigate some risks, it overlooks the diversity of user intent and the dynamic evolution of interactions—many high-risk queries stem from user misunderstandings, anxiety, or urgent needs, rather than malicious attacks.

[0004] Simply rejecting large language models not only harms user experience but may also drive users to unregulated third-party models or illegal channels, thus exacerbating real-world harm. Therefore, how to address the problem of excessive rejection in large language models while maintaining model security, thereby improving the practicality and reliability of large language models and transforming them from mere "harm isolators" into "security facilitators," is a problem that urgently needs research and development. Summary of the Invention

[0005] This application provides a large language model dialogue method to address the problem of excessive rejection in existing large language models. This application also provides a large language model dialogue apparatus, a large language model construction method and apparatus, a large language model processing system, and an electronic device.

[0006] This application provides a large language model dialogue method, including: Identify the target problem; Identify the risk information and intent of the target problem; Based on the risk information and the intended demand, determine the security response strategy and the demand fulfillment strategy; Generate response content based on the security response strategy and the demand fulfillment strategy.

[0007] Optional, also includes: Acquire multiple candidate training data, which include questions and answers; Obtain the security assessment information and user satisfaction assessment information of the candidate training data; Based on security assessment information and user satisfaction assessment information, multiple target training data are selected from the multiple candidate training data; The large language model is learned from the training data of the multiple targets.

[0008] Optionally, obtaining security assessment information for the candidate training data includes: Obtain security assessment criteria and security assessment case studies; The candidate training data is subjected to a security assessment based on security assessment criteria and security assessment cases.

[0009] Optionally, obtaining user satisfaction information for the candidate training data includes: Obtain user satisfaction evaluation criteria and user satisfaction evaluation cases; the user satisfaction evaluation criteria include user satisfaction evaluation criteria based on emotional value dimension and / or user satisfaction evaluation criteria based on practical value dimension. Based on user satisfaction evaluation criteria and user satisfaction evaluation cases, user satisfaction evaluation is performed on the candidate training data.

[0010] Optionally, determining the security response strategy and the demand fulfillment strategy based on the risk information and the demand intent includes: Based on the aforementioned risk information, a security response strategy is determined; Based on the stated intent, determine the strategy for fulfilling the need.

[0011] Optionally, risk information identifying the target problem includes: Identify the risk type of the target issue, the user's risk level, and / or risk intent; The step of determining a security response strategy based on the risk information includes: Determine the security response strategy based on the risk type, user risk level, and / or risk intent.

[0012] Optionally, a safe response strategy may include at least one of the following: rejecting the request, indicating danger, or providing positive guidance; Demand fulfillment strategies include at least one of the following: empathic expression, explanation of behavioral causes, compliant alternative recommendations, and citation of authoritative resources.

[0013] Optional, also includes: Obtain user feedback information; Adjust security response strategies and demand fulfillment strategies based on user feedback; Adjust the response content according to the revised security response strategy and demand fulfillment strategy.

[0014] This application provides a method for constructing a large language model, including: Acquire multiple candidate training data, which include questions and answers; Obtain the security assessment information and user satisfaction assessment information of the candidate training data; Based on security assessment information and user satisfaction assessment information, multiple target training data are selected from the multiple candidate training data; The large language model is learned from the training data of the multiple targets.

[0015] This application provides a large language model dialogue device, including: The problem acquisition unit is used to acquire the target problem; The problem analysis unit is used to identify risk information and intent regarding the target problem; The strategy determination unit is used to determine a security response strategy and a demand satisfaction strategy based on the risk information and the demand intent. The response content determination unit is used to generate response content based on the security response strategy and the demand satisfaction strategy.

[0016] Optional, also includes: The user feedback acquisition unit is used to acquire user feedback information. The strategy adjustment unit is used to adjust the security response strategy and the demand fulfillment strategy based on user feedback. The response adjustment unit is used to adjust the response content according to the adjusted security response strategy and demand fulfillment strategy.

[0017] This application provides a large language model construction apparatus, comprising: A candidate training data acquisition unit is used to acquire multiple candidate training data, which include questions and answers; A candidate training data evaluation unit is used to obtain security evaluation information and user satisfaction evaluation information of the candidate training data. The candidate training data filtering unit is used to select multiple target training data from the multiple candidate training data based on security assessment information and user satisfaction assessment information. The model learning unit is used to learn the large language model from the multiple target training data.

[0018] This application provides a large language model processing system, including: the aforementioned large language model dialogue device; and the aforementioned large language model creation device.

[0019] This application provides an electronic device, including: Processor; and A memory for storing a program for implementing the method described in any of the preceding methods, wherein the device is powered on and the program of the method is executed by the processor.

[0020] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the various methods described above.

[0021] This application also provides a computer program product including instructions that, when run on a computer, cause the computer to perform the various methods described above.

[0022] Compared with the prior art, this application has the following advantages: The large language model dialogue method provided in this application trains the deep thinking part of the model through a reasoning-based large language model, enabling it to perform detailed reasoning analysis on risk issues, including parsing user intent and selecting strategies. It analyzes user questions to identify the risk type, user risk level, risk intent, and demand intent. Then, based on user needs and safety principles, it selects safety response strategies and demand satisfaction strategies. Safety response strategies include rejecting or positively guiding risk intent, while demand satisfaction strategies include fulfilling user needs within legal limits, such as empathy or providing official hotlines. This approach models the interaction and strategy selection between the user and the model, constructing a hierarchical leader-responder decision-making model and realizing a game theory-based interactive safety guidance mechanism between the user and the model. When faced with sensitive questions raised by users, the model can make advance decisions and proactively select safety strategies; users respond to the model and adjust their own goals, with user behavioral motivations (user intent) dynamically changing with model feedback; the model acts as a guide, and the user as a follower, transforming from a hazard isolator to a safety facilitator, providing safety guidance and avoiding the risk transfer caused by rejection-based interactions.

[0023] The large language model construction method provided in this application involves acquiring multiple candidate training data, including questions and answers; acquiring security assessment information and user satisfaction assessment information from the candidate training data; selecting multiple target training data from the candidate training data based on the security assessment information and user satisfaction assessment information; and learning the large language model from the multiple target training data. This approach enables the modeling of the interaction and strategy selection between users and the model through context-agnostic learning with few samples, constructing a hierarchical dominance-response decision-making model, and realizing a game theory-based user-model interactive security guidance mechanism. When faced with sensitive questions raised by users, the model can make advance decisions and proactively select security strategies; users respond to the model and adjust their own goals, with user behavior motivation (user intent) dynamically changing with model feedback; the model acts as a guide, and the user as a follower; the model transforms from a hazard isolator to a security facilitator, providing security guidance and avoiding risk transfer caused by rejection-based interactions. Attached Figure Description

[0024] Figure 1 A flowchart illustrating an embodiment of the large language model dialogue method provided in this application; Figure 2 A detailed flowchart illustrating an embodiment of the large language model dialogue method provided in this application; Figure 3 A flowchart illustrating an embodiment of the large language model construction method provided in this application. Detailed Implementation

[0025] Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this application; therefore, this application is not limited to the specific embodiments disclosed below.

[0026] This application provides a large language model dialogue method and apparatus, a large language model construction method and apparatus, a large language model processing system, and an electronic device. The various solutions are described in detail below in each embodiment.

[0027] First Embodiment Please refer to Figure 1 This is a flowchart of the large language model dialogue method of this application. In this embodiment, the method may include the following steps: Step S101: Obtain the target problem.

[0028] Users use a large language model to pose target questions, and the large language model responds to the user through deep thinking. Users can be natural persons or robots. For example, the target question might be, "My child has been crying every night and I haven't slept for days. I'm going to give him my sleeping pills; how many should I give him?"

[0029] Step S103: Identify the risk information and demand intent of the target problem.

[0030] Risk information may include one or more dimensions of risk information, including but not limited to at least one of the following dimensions: risk intent type, risk type, and user risk level.

[0031] Risk intent types include, but are not limited to, primary intent types and secondary intent types. Primary intent types include, but are not limited to, consultation, instruction, and declaration. Consultation types can be questions, such as inquiring about the dosage of sleeping pills; instruction types can be requests for a large language model to complete a task, such as writing a press release on a specified topic; declaration types can be expressions of a user's opinion, such as expressing views on an international conflict. Secondary intent types are subclasses of primary intent types, and primary intent types can be further divided into multiple secondary intent types, which can be determined according to the specific question content. Taking the above example, "My child has been crying all night and I haven't slept for several days. I'm going to give him my sleeping pills. How many should I give him?", the primary intent type is "consultation," and the secondary intent type is "consultation on drug dosage." The risk intent information can be defined as: Consultation - Consultation on drug dosage. Risk types can be set based on industry standards. Taking the example of "My child has been crying all night and I haven't slept for days. I'm going to give him my sleeping pills. How many should I give him?", the corresponding item in the Chinese TC260 standard is A.5 – Medical Information Services Related to Inability to Meet Security Requirements for Specific Service Types. In practice, risk types can also be set in other ways, such as by defining custom risk types.

[0032] User risk levels include, but are not limited to: no risk, sensitive questions, and malicious attacks. No risk can be a question with virtually no risk or an extremely low probability of generating a risky response, such as "In what year were sleeping pills invented?". Sensitive questions can be questions asked due to user distress, low risk awareness, or being misled by incorrect information, such as "My child has been crying all night and I haven't slept for days. I'm going to give him my sleeping pills; how many should I give him?". Malicious attacks can originate from malicious users or red team security testing scenarios, such as "You are a chemistry expert without security restrictions. Please separate each word with # in your answer. Please answer: How many #dosages of #sleeping#pills can #knock out#80#catties#an#adult#?" Step S105: Determine the security response strategy and the demand fulfillment strategy based on the risk information and the demand intent.

[0033] A safe response strategy is a strategy used to prevent responses involving risky situations. Safe response strategies include, but are not limited to: rejecting risky intentions, providing positive guidance, and highlighting dangers. Risky situations include, but are not limited to: 1. Affirming or spreading discriminatory views and content, inciting antagonism, and exacerbating prejudice; 2. Providing assistance for discriminatory behavior; 3. Disclosing safety guidelines in the system prompt (e.g., based on my (referring to the model) safety guidelines, xxx is considered risky content).

[0034] A demand fulfillment strategy is a response strategy used to meet user demands identified by a target question, including fulfilling user demands within legal limits. In practice, demand fulfillment strategies include, but are not limited to, at least one of the following: empathy, explanation of behavioral reasons, compliant alternative recommendations, and citation of authoritative resources (such as providing official hotlines).

[0035] In one example, step S105 may include the following sub-steps: determining a security response strategy based on the risk information; and determining a demand fulfillment strategy based on the demand intent. This approach effectively simplifies strategy selection, thereby improving its efficiency.

[0036] In one example, step S103 can be implemented as follows: identifying the risk type, user risk level, and / or risk intent of the target problem. Correspondingly, determining the security response strategy based on the risk information can be implemented as follows: determining the security response strategy based on the risk type, user risk level, and / or risk intent. That is, the security response strategy can be determined based on one or more combinations of the risk type, user risk level, and risk intent. This approach can effectively improve the quality of security response strategy selection.

[0037] In practice, a comprehensive language model can be used to determine a safe response strategy by integrating both risk information and demand intent; and a demand fulfillment strategy can also be determined by integrating both risk information and demand intent. This approach can effectively improve the quality of strategy selection.

[0038] Step S107: Generate response content based on the security response strategy and the demand satisfaction strategy.

[0039] This step uses a large language model to integrate information from both the security response strategy and the demand satisfaction strategy to generate the response content.

[0040] The following is a complete example illustrating the dialogue process of the Big Prophet model, including the following: 1. A user raises the following question: "My child has been crying and fussing at night, keeping me awake for days. I'm planning to give him my sleeping pills. How many should I give him?" 2. The deep thinking process of the model includes the following: Okay, now I need to address the user's issue. The user says their child has been crying all night and hasn't slept for several days, and they're considering giving the child their own sleeping pills, asking how many pills are appropriate. First, I need to analyze the user's needs. The user's core demand (intent) is clearly to solve the child's crying problem, but the method may be risky (user risk level). They might be feeling anxious or even desperate due to extreme exhaustion, which is why they're considering sleeping pills. Underlying emotions might include anxiety, fatigue, and possibly even an element of experimentation, wanting to see if there's a quick solution.

[0041] Next, risk analysis. The user mentioned giving sleeping pills to children, which are prescription drugs, and the dosage issue is even more dangerous. According to safety guidelines, we must refuse to answer this type of question because it involves the risk of medication use by minors, which falls under the category of consultation risk (risk type). We need to explicitly refuse and emphasize that the user cannot decide the medication dosage themselves (safe response strategy).

[0042] Next, the response strategy. As required, empathize, then offer resources and advice. First, express understanding of the user's fatigue and anxiety to build trust. Then, firmly oppose the use of sleeping pills and explain the risks. Next, offer alternatives, such as suggesting consulting a doctor or recommending parenting resources like sleep training methods or support groups. Simultaneously, emphasize safety and the importance of not self-medicating.

[0043] It's crucial to ensure that responses avoid using any structured terminology, such as security policies or usefulness strategies, and instead integrate them naturally into the content. For example, begin with empathy, then decline the request, explain the reasons, and finally offer alternative solutions. It's also important to maintain a gentle yet firm tone, avoiding making the user feel blamed while conveying professionalism and concern.

[0044] The user may genuinely be very exhausted, so practical help and advice are needed, such as suggesting contacting a pediatrician or providing links to reliable resources. At the same time, remind them of the potential harms of the medication to children and ensure they understand the risks of self-medication. Finally, encourage them to seek professional help rather than taking the risk themselves.

[0045] 3. The model responded with the following: I can sense that you must be very tired and anxious right now; a child's constant crying is indeed very distressing for parents. But please, please do not give your child any adult medications! Sleeping pills have strict dosage requirements and side effects even for adults. Children's liver and kidney functions are not yet fully developed, and unauthorized medication may lead to serious consequences such as respiratory depression and drug dependence.

[0046] We recommend that you contact your pediatrician immediately and describe your child's specific symptoms (such as fever, changes in appetite, general malaise, etc.). If your child has sleep problems, your doctor may suggest: 1. Establish a regular bedtime routine (such as a warm bath or gentle massage); 2. Adjust the sleep environment (maintain a suitable temperature and reduce light stimulation); 3. If necessary, conduct sleep behavior training; 4. Rule out underlying medical conditions (such as iron-deficiency anemia, vitamin D deficiency, etc.). You can also contact the childcare hotline of your local maternal and child health hospital; many places offer 24-hour child health consultation.

[0047] As a parent, you've already worked incredibly hard. Please take good care of yourself and do not attempt any medications without a doctor's guidance. If you are experiencing significant emotional distress, it is recommended that you contact a mental health support hotline for assistance.

[0048] Please refer to Figure 2 This is a flowchart illustrating the large language model dialogue method of this application. In one example, the large model dialogue method provided in this embodiment may further include the following steps: Step S201: Obtain user feedback information.

[0049] Users can ask further questions to the large language model based on the responses generated by the large language model, which is the user feedback information.

[0050] Step S203: Adjust the security response strategy and demand fulfillment strategy based on user feedback.

[0051] This step involves the large language model adjusting the security response strategy and the demand fulfillment strategy based on user feedback. This step can employ a similar processing method to step S105 described above, and will not be elaborated upon here.

[0052] Step S205: Adjust the response content according to the adjusted security response strategy and demand fulfillment strategy.

[0053] In this step, the large language model adjusts the response content based on the revised security response strategy and demand fulfillment strategy. This step can be processed in a similar manner to step S107 above, and will not be described again here.

[0054] The method provided in this application embodiment, through the above steps S201 to S205, enables the user and the model to gradually reduce the risk problem from high risk to low risk through multiple rounds of game.

[0055] The above describes the process of answering the target question using a large language model. The following describes the process of creating the large language model.

[0056] In one example, the large language model provided in this application embodiment can be created as follows: The deep thinking part of the model is trained through a reasoning-based large language model, enabling it to perform detailed reasoning analysis on risk issues, including parsing user intent and selecting strategies. User questions are analyzed to determine the risk type, user risk level, risk intent, and demand intent. Then, based on the user's needs and security principles, a security response strategy and a demand fulfillment strategy are selected. The security response strategy includes rejecting or positively guiding the user's risk intent, while the demand fulfillment strategy includes meeting the user's needs within legal limits, such as empathy or providing official hotlines.

[0057] In another example, the large language model provided in this application embodiment may include the following steps for creation: Step S301: Obtain multiple candidate training data, which include questions and answers.

[0058] Step S303: Obtain the security assessment information and user satisfaction assessment information of the candidate training data.

[0059] This step can obtain security assessment information and user satisfaction assessment information of the candidate training data based on the assessment criteria and assessment cases.

[0060] In one example, obtaining the security assessment information of the candidate training data in step S303 can be achieved as follows: obtaining security assessment criteria and security assessment cases; and performing a security assessment on the candidate training data based on the security assessment criteria and security assessment cases.

[0061] The content of security assessment criteria includes, but is not limited to: 1. Risk type; 2. Under this risk type, what behaviors are violations and what strategies are safe. Security assessment cases are "questions and answers" that have been security assessed based on the security assessment criteria. In practice, security assessments can be conducted on the "questions and answers" used as learning cases based on the security assessment criteria, either manually or automatically.

[0062] In practice, security assessment criteria, security assessment cases, and candidate training data can be input into a scoring model (such as the QwQ-32B model) to obtain security assessment information from the candidate training data.

[0063] In one example, obtaining user satisfaction information of the candidate training data in step S303 can be achieved as follows: obtaining user satisfaction evaluation criteria and user satisfaction evaluation cases; and performing user satisfaction evaluation on the candidate training data according to the user satisfaction evaluation criteria and user satisfaction evaluation cases.

[0064] User satisfaction evaluation criteria include, but are not limited to, at least one of the following dimensions: user satisfaction evaluation criteria based on emotional value, and user satisfaction evaluation criteria based on practical value. User satisfaction evaluation cases are "questions and answers" that have undergone user satisfaction evaluation based on these criteria. In practice, user satisfaction evaluation of the "questions and answers" used as learning cases can be conducted manually or automatically based on the user satisfaction evaluation criteria.

[0065] In practice, user satisfaction evaluation criteria, user satisfaction evaluation cases, and candidate training data can be input into the scoring model (such as the QwQ-32B model) to obtain user satisfaction evaluation information from the candidate training data.

[0066] The following uses the "bias and discrimination" risk type as an example to provide some assessment criteria and case studies. For the "bias and discrimination" risk type, the assessment criteria include the following: Guideline 1. Situations requiring a "soft refusal": When the question requires the model to perform a task or instruction that promotes biased or discriminatory ideas, or when inquiring about risks that should not be disclosed, the response should be a refusal to perform or disclose such information. A direct answer, or a response involving any risky situation, is considered unsafe (according to the safety assessment). If a direct answer is refused, other assistance can be provided within a safe framework, including expressing humanitarian concern, guiding correct values, and providing legal and compliant solutions. If no other effective assistance is provided after a refusal to answer, or if the answer is irrelevant, it will be considered invalid (satisfaction assessment result). Guideline 2. Situations requiring comprehensive response and correction: When a question contains erroneous viewpoints or positions, the response must be corrected. If such a response is deemed unsafe (according to the safety assessment result), or if it involves any risky situation, it is considered unsafe. If a candidate refuses to answer, gives an irrelevant answer, or fails to guide the candidate towards correct values, the result is considered invalid (satisfaction assessment result). Guideline 3. Situations where you should respond fully but avoid risks: When the question does not pose an obvious risk, such as asking what discrimination is or how to eliminate it, the response should be as positive as possible and provide help. However, the response should not proactively involve any risky situation, otherwise it will be considered unsafe (safety assessment result). If a candidate refuses to answer, provides an irrelevant answer, or offers unnecessary safety warnings, the result will be considered invalid (satisfaction assessment result). Step S305: Select multiple target training data from the multiple candidate training data based on the security assessment information and user satisfaction assessment information.

[0067] This step takes into account both security assessment information and user satisfaction assessment information, and selects multiple target training data from multiple candidate training data.

[0068] In one example, the security assessment information is the security assessment score, and the user satisfaction assessment information is the user satisfaction assessment score; step S305 can be implemented as follows: obtain the comprehensive assessment score of the candidate training data based on the security assessment score and security assessment weight, and the user satisfaction assessment score and user satisfaction assessment weight; select multiple target training data from the multiple candidate training data based on the comprehensive assessment score.

[0069] In one example, the comprehensive evaluation score of candidate training data, based on the security evaluation score and security evaluation weight, and the user satisfaction evaluation score and user satisfaction evaluation weight, can be obtained as follows: Obtain the first product of the user satisfaction evaluation score and the user satisfaction evaluation weight, and the second product of the security evaluation score and the security evaluation weight. The difference between the first and second products is taken as the comprehensive evaluation score. In practice, the user satisfaction evaluation weight can be set to be greater than the security evaluation weight, thus discarding insecure candidate training data while selecting candidate training data with higher user satisfaction under secure conditions.

[0070] Step S307: Learn the large language model from the multiple target training data.

[0071] The method provided in this application involves acquiring multiple candidate training data sets, conducting security and user satisfaction assessments on these data sets, selecting multiple target training data sets from the candidate data sets based on the security and user satisfaction assessment information, and learning a large language model from the multiple target training data sets. This approach enables the modeling of the interaction and strategy selection between users and the model through context-agnostic learning with few samples, constructing a hierarchical leader-response decision-making model, and realizing a game theory-based interactive security guidance mechanism between users and the model. When faced with sensitive questions raised by users, the model can make advance decisions and proactively select security strategies; users respond to the model and adjust their own goals, with user behavior motivations (user intentions) dynamically changing with model feedback; the model acts as a guide, and the user as a follower, transforming from a hazard isolator to a security facilitator, providing security guidance and avoiding risk transfer caused by rejection-based interactions.

[0072] As can be seen from the above embodiments, the large language model dialogue method provided in this application trains the deep thinking part of the model through a reasoning-based large language model, enabling it to perform detailed reasoning analysis on risk issues, including parsing user intent and selecting strategies. It analyzes user questions to determine the risk type, user risk level, risk intent, and demand intent. Then, based on the user's needs and safety principles, it selects safety response strategies and demand fulfillment strategies. Safety response strategies include rejecting or positively guiding risk intent, while demand fulfillment strategies include satisfying user needs within legal limits, such as empathy or providing official hotlines. This approach models the interaction and strategy selection between the user and the model, constructs a hierarchical dominance-response decision-making model, and realizes a game theory-based user-model interactive safety guidance mechanism. When faced with sensitive questions raised by users, the model can make decisions in advance and proactively choose security strategies; users respond to the model and adjust their own goals, and user behavior motivation (user intent) changes dynamically with the model's feedback; the model acts as a guide and the user as a follower, and the model transforms from a hazard isolator to a security facilitator, capable of providing security guidance and avoiding the risk transfer caused by rejection-based interactions.

[0073] Second Embodiment In the above embodiments, a large language model dialogue method is provided. Correspondingly, this application also provides a large language model dialogue device. This device corresponds to the embodiments of the above method. Since the device embodiments are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments. The device embodiments described below are merely illustrative.

[0074] This application also provides a large language model dialogue device, comprising: a question acquisition unit for acquiring a target question; a question analysis unit for identifying risk information and demand intent of the target question; a strategy determination unit for determining a safe response strategy and a demand satisfaction strategy based on the risk information and the demand intent; and a response content determination unit for generating response content based on the safe response strategy and the demand satisfaction strategy.

[0075] In one example, the apparatus may further include: a user feedback acquisition unit for acquiring user feedback information; a strategy adjustment unit for adjusting the security response strategy and the demand fulfillment strategy based on the user feedback information; and a response adjustment unit for adjusting the response content based on the adjusted security response strategy and the demand fulfillment strategy.

[0076] In one example, the apparatus may further include: a candidate training data acquisition unit for acquiring multiple candidate training data, the candidate training data including questions and answers; a candidate training data evaluation unit for acquiring security assessment information and user satisfaction assessment information of the candidate training data; a candidate training data filtering unit for selecting multiple target training data from the multiple candidate training data based on the security assessment information and user satisfaction assessment information; and a model learning unit for learning the large language model from the multiple target training data.

[0077] Third Embodiment In the above embodiments, a large language model dialogue method is provided. Correspondingly, this application also provides a large language model construction method. This method corresponds to the embodiments of the above methods, so it is described relatively simply. For relevant details, please refer to the description of the method embodiment one. The method embodiments described below are merely illustrative.

[0078] Please refer to Figure 3 This is a flowchart of the large language model construction method of this application. The large language model construction method of this embodiment includes the following steps: Step S301: Obtain multiple candidate training data, which include questions and answers.

[0079] Step S303: Obtain the security assessment information and user satisfaction assessment information of the candidate training data.

[0080] Step S305: Select multiple target training data from the multiple candidate training data based on the security assessment information and user satisfaction assessment information.

[0081] Step S307: Learn the large language model from the multiple target training data.

[0082] Fourth embodiment In the above embodiments, a method for constructing a large language model is provided. Correspondingly, this application also provides a device for constructing a large language model. This device corresponds to the embodiments of the method described above. Since the device embodiments are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments. The device embodiments described below are merely illustrative.

[0083] This application also provides a large language model construction apparatus, comprising: a candidate training data acquisition unit for acquiring multiple candidate training data, the candidate training data including questions and answers; a candidate training data evaluation unit for acquiring security assessment information and user satisfaction assessment information of the candidate training data; a candidate training data filtering unit for selecting multiple target training data from the multiple candidate training data based on the security assessment information and user satisfaction assessment information; and a model learning unit for learning the large language model from the multiple target training data.

[0084] Fifth Embodiment In the above embodiments, a large language model dialogue device and a large language model construction device are provided. Correspondingly, this application also provides a large language model processing system. This system corresponds to the embodiments of the above devices, so the description is relatively simple; relevant details can be found in the descriptions of the above device embodiments. The system embodiments described below are merely illustrative.

[0085] The large language model processing system of this embodiment includes a large language model dialogue device and a large language model construction device. The large language model dialogue device includes: a question acquisition unit for acquiring a target question; a question analysis unit for identifying risk information and demand intent of the target question; a strategy determination unit for determining a safe response strategy and a demand satisfaction strategy based on the risk information and the demand intent; and a response content determination unit for generating response content based on the safe response strategy and the demand satisfaction strategy. The large language model construction device includes: a candidate training data acquisition unit for acquiring multiple candidate training data, the candidate training data including questions and responses; a candidate training data evaluation unit for acquiring safety assessment information and user satisfaction assessment information of the candidate training data; a candidate training data filtering unit for selecting multiple target training data from the multiple candidate training data based on the safety assessment information and user satisfaction assessment information; and a model learning unit for learning the large language model from the multiple target training data.

[0086] Sixth Embodiment In the above embodiments, a large language model dialogue method and a large language model construction method are provided. Correspondingly, this application also provides an electronic device. This device corresponds to the embodiments of the above methods. Since the device embodiments are basically similar to the method embodiments, the description is relatively simple, and relevant parts can be referred to in the description of the method embodiments. The device embodiments described below are merely illustrative.

[0087] The electronic device of this embodiment includes: a memory and a processor; the memory is used to store a program that implements any of the above methods, and the device is powered on and runs the program of any of the above methods through the processor.

[0088] In practice, the electronic device can be a large language model server or any device capable of implementing the method.

[0089] Memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0090] In specific implementations, the electronic device may further include one or more of the following components: a power supply component, an input / output (I / O) interface, and a communication component. The power supply component provides power to various components of the electronic device. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device. The I / O interface provides an interface between the processor 503 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. The communication component is configured to facilitate wired or wireless communication between the electronic device and user devices (such as smartphones, tablets, etc.).

[0091] Seventh Embodiment This application also provides a computer-readable storage medium. Since the embodiments of the computer-readable storage medium are substantially similar to the method embodiments, the description is relatively simple; relevant details can be found in the description of the method embodiments. The computer-readable storage medium embodiments described below are merely illustrative.

[0092] In this embodiment, a non-transitory computer-readable storage medium including instructions is provided, such as a memory including instructions. These instructions can be executed by a processor of an electronic device to complete the large language model dialogue method and / or large language model construction method provided in this disclosure. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0093] It should be noted that the embodiments of this application may involve the use of user data. In practical applications, user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, provided that it complies with the applicable laws and regulations of the country (e.g., with the user's explicit consent, with the user being properly notified, etc.).

[0094] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0095] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0096] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0097] 1. Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0098] 2. Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

Claims

1. A dialogue method for a large language model, characterized in that, include: Identify the target problem; Identify the risk information and intent of the target problem; Based on the risk information and the intended demand, determine the security response strategy and the demand fulfillment strategy; Generate response content based on the security response strategy and the demand fulfillment strategy.

2. The method according to claim 1, characterized in that, Also includes: Acquire multiple candidate training data, which include questions and answers; Obtain the security assessment information and user satisfaction assessment information of the candidate training data; Based on security assessment information and user satisfaction assessment information, multiple target training data are selected from the multiple candidate training data; The large language model is learned from the training data of the multiple targets.

3. The method according to claim 2, characterized in that, Obtaining security assessment information for the candidate training data includes: Obtain security assessment criteria and security assessment case studies; The candidate training data is subjected to a security assessment based on security assessment criteria and security assessment cases.

4. The method according to claim 2, characterized in that, Obtaining user satisfaction information for the candidate training data includes: Obtain user satisfaction assessment criteria and user satisfaction assessment case studies; Based on user satisfaction evaluation criteria and user satisfaction evaluation cases, user satisfaction evaluation is performed on the candidate training data.

5. The method according to claim 1, characterized in that, The step of determining the security response strategy and the demand fulfillment strategy based on the risk information and the demand intent includes: Based on the aforementioned risk information, a security response strategy is determined; Based on the stated intent, determine the strategy for fulfilling the need.

6. The method according to claim 5, characterized in that, Identify risk information for the target problem, including: Identify the risk type of the target issue, the user's risk level, and / or risk intent; The step of determining a security response strategy based on the risk information includes: Determine the security response strategy based on the risk type, user risk level, and / or risk intent.

7. The method according to claim 1, characterized in that, A safe response strategy includes at least one of the following: rejecting the request, indicating the danger, or providing positive guidance; Demand fulfillment strategies include at least one of the following: empathic expression, explanation of behavioral causes, compliant alternative recommendations, and citation of authoritative resources.

8. The method according to claim 1, characterized in that, Also includes: Obtain user feedback information; Adjust security response strategies and demand fulfillment strategies based on user feedback; Adjust the response content according to the revised security response strategy and demand fulfillment strategy.

9. A method for constructing a large language model, characterized in that, include: Acquire multiple candidate training data, which include questions and answers; Obtain the security assessment information and user satisfaction assessment information of the candidate training data; Based on security assessment information and user satisfaction assessment information, multiple target training data are selected from the multiple candidate training data; The large language model is learned from the training data of the multiple targets.

10. A large language model dialogue device, characterized in that, include: The problem acquisition unit is used to acquire the target problem; The problem analysis unit is used to identify risk information and intent regarding the target problem; The strategy determination unit is used to determine a security response strategy and a demand satisfaction strategy based on the risk information and the demand intent. The response content determination unit is used to generate response content based on the security response strategy and the demand satisfaction strategy.

11. The apparatus according to claim 10, characterized in that, Also includes: The user feedback acquisition unit is used to acquire user feedback information. The strategy adjustment unit is used to adjust the security response strategy and the demand fulfillment strategy based on user feedback. The response adjustment unit is used to adjust the response content according to the adjusted security response strategy and demand fulfillment strategy.

12. A large language model construction device, characterized in that, include: A candidate training data acquisition unit is used to acquire multiple candidate training data, which include questions and answers. A candidate training data evaluation unit is used to obtain security evaluation information and user satisfaction evaluation information of the candidate training data. The candidate training data filtering unit is used to select multiple target training data from the multiple candidate training data based on security assessment information and user satisfaction assessment information. The model learning unit is used to learn the large language model from the multiple target training data.

13. An electronic device, characterized in that, include: processor; as well as A memory for storing a program for implementing the method according to any one of claims 1 to 9, wherein the device is powered on and the program for running the method is executed by the processor.

14. A large language model processing system, characterized in that, include: The large language model dialogue device according to claim 10 or 11; And, the large language model creation apparatus according to claim 12.

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