A method and system for implementing scenario-based robots with different conversation styles
By integrating character design and script to generate prompt words, and using large model processing modules to generate dialogues, the problems of high resource consumption, poor flexibility and insufficient versatility in the existing technology are solved, and the effect of quickly generating diversified dialogue styles and improving development efficiency is achieved.
Patent Information
- Application Number
- CN202411720616.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-28
- Publication Date
- 2025-04-01
AI Technical Summary
When generating dialogue systems with specific roles or dialogue styles, the prior art consumes a lot of resources, is poor in flexibility, and is not versatile, making it difficult to quickly respond to market demand and adapt to new scenarios.
The information fusion module integrates the character and script, generates prompt words, and uses the big model processing module to process the prompt words and dialogue content to generate reply dialogue. This method does not require complex model training or large-scale data fine-tuning, and can quickly generate diverse conversation styles.
It has realized the rapid construction of a dialogue robot system for different application scenarios, improved development efficiency and market response speed, and the generated dialogue content is highly consistent with the needs of the scenario and is even better in serious business scenarios.
Smart Images
Figure CN119739821A_ABST
Abstract
Description
Background Art
[0002] The closest existing technology is the role-playing dialogue generation technology based on a large language model. According to the current application status, this technology can be roughly divided into two technical routes:
[0003] 1. Fine-tuning techniques to change large model parameters
[0004] (1) Technical Overview: This type of technology uses specific dialogue data to retrain the model during the training and fine-tuning stages of the large model, allowing the large model to learn the dialogue style of a specific scenario. For example, the CharacterGLM team collected a dataset containing character attributes and behavior descriptions and fine-tuned the model to create a dialogue system that can imitate the style of a specific character. This type of technology is usually based on large models such as ChatGLM, and generates dialogues of a specific style by learning from a large amount of specific data.
[0005] (2) Application status: This type of technology is widely used in scenarios that require simulation of specific roles or dialogue styles, such as virtual character dialogue generation, game NPC dialogue simulation, etc. Through model fine-tuning, large models can be given stronger specific style dialogue capabilities, making the model output more suitable for specific roles or tasks.
[0006] (3) Technical defects:
[0007] 1) High resource consumption: Each time a new scene or character style is adapted, a large amount of data needs to be collected and the model fine-tuned and trained, which is a time-consuming and labor-intensive process.
[0008] 2) Poor flexibility: Once the model is fine-tuned, its style and application scenarios are relatively fixed, and it cannot quickly respond to changes in market demand and the emergence of new scenarios.
[0009] 3) Lack of universality: Different business scenarios often require specialized training data, which cannot be used across scenarios, especially in the generation of professional dialogues in specific industries, and its application is limited.
[0010] 2. Non-model parameter modification technology based on prompt word engineering
[0011] (1) Technical Overview: This type of technical route does not change the structure and parameters of the large language model, but uses prompt engineering to guide the large model to simulate different conversation styles. This method uses pre-designed prompts to allow the large model to output conversations that conform to a specific style without retraining the model. For example, the ChatHaruhi team guided the large model to generate conversations that simulate the style of anime characters by adding the specific conversation style of anime characters to the prompts.
[0012] (2) Application status: Prompt word engineering technology is currently mainly used in some informal scenarios, such as virtual assistants, social chat robots, game dialogues, etc. The advantage of this type of technology is its flexibility, which can quickly generate dialogues that meet the expected style without the need for large-scale model training.
[0013] (3) Technical defects:
[0014] 1) Lack of professionalism: Since prompt word engineering mainly relies on the general capabilities of large models, although it can quickly generate dialogues, it often lacks deep scenario-based planning capabilities in scenarios with high professional requirements (such as sales, customer service, etc.), and is prone to producing dialogues that do not meet scenario requirements.
[0015] 2) Lack of scenario-based performance: Existing technologies are mostly oriented towards small talk tasks. Although they can generate consistent and personified conversations, they perform relatively poorly in serious business scenarios (such as customer service and sales) and lack detailed scenario settings, resulting in conversations that are not realistic enough or do not meet user expectations.
[0016] 3) Flexibility limitations: Although prompt word engineering can guide the conversation style to a certain extent, it lacks deep customization of conversation scenarios and is difficult to flexibly respond to diverse user needs in complex business scenarios. Summary of the invention
[0017] The present application provides a method and system for implementing scenario-based robots with different conversation styles, so as to quickly build a conversation robot system for different application scenarios and improve product development efficiency and market response speed.
[0018] In a first aspect, a method for implementing scenario-based robots with different conversation styles is provided, comprising the following steps:
[0019] Use the information fusion module to fuse the character setting and the script to generate prompt words;
[0020] Using the dialogue input module to input dialogue content;
[0021] The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
[0022] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0023] In a specific embodiment, it also includes:
[0024] Use the character generation module to set the robot's character;
[0025] The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
[0026] In a specific embodiment, it also includes:
[0027] Use the dialogue output module to display the dialogue.
[0028] In a specific implementation scheme, the large model processing module includes a large language model.
[0029] In a second aspect, a robot dialogue system based on a large language model is provided, comprising:
[0030] Information fusion module, used to integrate character settings and scripts and generate prompt words;
[0031] A dialogue input module, used for inputting dialogue content;
[0032] The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
[0033] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0034] In a specific embodiment, it also includes:
[0035] The character generation module is used to set the character of the robot;
[0036] The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
[0037] In a specific embodiment, it also includes:
[0038] The dialogue output module is used to display the dialogue.
[0039] In a specific implementation scheme, the large model processing module includes a large language model.
[0040] According to a third aspect, an electronic device is provided, comprising a processor, the processor being coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor so that the electronic device implements any one of the methods for implementing scenario-based robots with different conversation styles.
[0041] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0042] In a fourth aspect, a computer-readable storage medium is provided, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor so that the computer-readable storage medium implements the method of realizing scenario-based robots with different dialogue styles.
[0043] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] Figure 1 A flowchart of a method for implementing scenario-based robots with different conversation styles provided in an embodiment of the present application;
[0045] Figure 2 A structural block diagram of a robot dialogue system based on a large language model provided in an embodiment of the present application;
[0046] Figure 3 A specific flow chart of a method for implementing scenario-based robots with different conversation styles provided in an embodiment of the present application. DETAILED DESCRIPTION
[0047] The present application is further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present application will become clearer and more specific.
[0048] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.
[0049] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.
[0050] To facilitate understanding of the method and system for implementing scenario-based robots with different conversation styles provided in the embodiments of the present application, its application scenarios are first explained. The method and system for implementing scenario-based robots with different conversation styles provided in the embodiments of the present application are used to quickly build a conversation robot system for different application scenarios, thereby improving product development efficiency and market response speed. The closest existing technology is the role-playing conversation generation technology based on a large language model. According to the current application status, this technology can be roughly divided into two technical routes: 1. Fine-tuning technology for changing large model parameters: (1) Technical overview: This type of technology uses specific conversation data to retrain the model during the training and fine-tuning stages of the large model, so that the large model learns the conversation style of a specific scenario. For example, the CharacterGLM team collects data sets containing character attributes and behavior descriptions and fine-tunes the model to create a conversation system that can imitate the style of a specific character. This type of technology is usually based on large models such as ChatGLM, and generates conversations of a specific style by learning a large amount of specific data. (2) Application status: This type of technology is widely used in scenarios where specific characters or conversation styles need to be simulated, such as virtual character conversation generation, game NPC conversation simulation, etc. Through model fine-tuning, the large model can be equipped with stronger specific style dialogue capabilities, making the model output more suitable for specific roles or tasks. (3) Technical defects: 1) High resource consumption: Each time a new scene or role style is adapted, a large amount of data collection and model fine-tuning training is required, which is time-consuming and labor-intensive. 2) Poor flexibility: Once the model is fine-tuned, its style and application scenario are relatively fixed, and it cannot quickly respond to changes in market demand and the emergence of new scenarios. 3) Insufficient versatility: Different business scenarios often require special training data, which cannot be universal across scenarios, especially in the generation of professional dialogues in specific industries, where its application is limited. 2. Non-model parameter modification technology based on prompt engineering: (1) Technical overview: This type of technical route does not change the structure and parameters of the large language model, but uses prompt engineering to guide the large model to simulate different dialogue styles. This method uses pre-designed prompts to allow the large model to output dialogues that conform to a specific style without retraining the model. For example, the ChatHaruhi team guides the large model to generate dialogues that simulate the style of anime characters by adding the specific dialogue style of anime characters to the prompts. (2) Application status: Prompt word engineering technology is currently mainly used in some informal scenarios, such as virtual assistants, social chat robots, game dialogues, etc. The advantage of this type of technology is its flexibility, which can quickly generate dialogues that meet the expected style without the need for large-scale model training.(3) Technical defects: 1) Lack of professionalism: Since the prompt word engineering mainly relies on the general capabilities of large models, although it can quickly generate dialogues, it often lacks deep scenario planning capabilities in scenarios with high professional requirements (such as sales, customer service, etc.), and it is easy to produce dialogues that do not meet the needs of the scenario. 2) Lack of scenario performance: Existing technologies are mostly oriented towards small talk tasks. Although they can generate consistent and personified dialogues, their performance in serious business scenarios (such as customer service and sales) is relatively weak, lacking detailed scenario settings, resulting in dialogues that are not real enough or do not meet user expectations. 3) Flexibility limitations: Although the prompt word engineering can guide the dialogue style to a certain extent, it lacks deep customization of the dialogue scene, making it difficult to flexibly respond to diverse user needs in complex business scenarios. To this end, the embodiment of the present application provides a method and system for realizing scenario-based robots with different dialogue styles, so as to quickly build a dialogue robot system for different application scenarios and improve product development efficiency and market response speed. The following is a detailed description of the embodiment in conjunction with specific drawings.
[0051] refer to Figures 1 to 3 , Figure 1 A flowchart of a method for implementing scenario-based robots with different conversation styles provided in an embodiment of the present application; Figure 2 A structural block diagram of a robot dialogue system based on a large language model provided in an embodiment of the present application; Figure 3 A specific flow chart of a method for implementing scenario-based robots with different conversation styles provided in an embodiment of the present application.
[0052] exist Figure 1 In the embodiment of the present application, a method for implementing scenario-based robots with different conversation styles is provided, comprising the following steps:
[0053] Use the information fusion module to fuse the character setting and the script to generate prompt words;
[0054] Using the dialogue input module to input dialogue content;
[0055] The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
[0056] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0057] In a specific embodiment, it also includes:
[0058] Use the character generation module to set the robot's character;
[0059] The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
[0060] In a specific embodiment, it also includes:
[0061] Use the dialogue output module to display the dialogue.
[0062] In a specific implementation scheme, the large model processing module includes a large language model.
[0063] Specifically, refer to Figure 3 , a method for implementing scenario-based robots with different conversation styles includes the following steps:
[0064] The robot configuration personnel operates the character setting generation module to set the character setting of the robot, and sends the character setting to the information fusion module;
[0065] The robot configuration personnel operates the dialogue scene generation module to set the sub-dialogue scenes and the series relationship of the sub-dialogue scenes to form a dialogue script, and sends the script to the information fusion module;
[0066] The information fusion module fuses the character setting and the script to generate prompt words that can be understood by the big model, and sends the prompt words to the big model processing module;
[0067] The robot user operates the dialogue input module to input the dialogue content, and sends the dialogue content to the large model processing module;
[0068] The large model processing module processes the prompt words and the dialogue content, generates a dialogue to reply to the robot user, and sends the dialogue to the dialogue output module;
[0069] The dialogue output module displays the dialogue, and the robot user can see the robot's reply dialogue, and can continue to operate the dialogue input module to input the dialogue content to complete multiple rounds of dialogue interaction with the robot.
[0070] In the above technical solution, first, the fusion setting of roles and scripts realizes the professionalism and personalization of dialogue generation: the core of this technology lies in the combination of scripts and role settings, and the output of the large language model is constrained and guided by the arrangement of dialogue scripts and the definition of personality (role setting). The script setting is responsible for the framework of the scene, ensuring the logic, coherence, professionalism and authenticity of the dialogue content in different scenarios; the role setting gives the robot personality and style, so that it can show a specific personalized dialogue style in interaction with users, such as seriousness, humor, professionalism, etc. Second, based on the prompt word engineering, the authenticity and diversity of the dialogue style are efficiently realized: without adjusting the parameters of the large model, the contextual learning ability of the large model is fully utilized through effective and reasonable prompt word engineering, and a variety of dialogue styles can be quickly generated. This scenario-based generation can not only improve the authenticity of the dialogue, but also adapt to the needs of different business scenarios.
[0071] exist Figure 2 In the embodiment of the present application, a robot dialogue system based on a large language model is provided, comprising:
[0072] Information fusion module, used to integrate character settings and scripts and generate prompt words;
[0073] A dialogue input module, used for inputting dialogue content;
[0074] The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
[0075] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0076] In a specific embodiment, it also includes:
[0077] The character generation module is used to set the character of the robot;
[0078] The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
[0079] In a specific embodiment, it also includes:
[0080] The dialogue output module is used to display the dialogue.
[0081] In a specific implementation scheme, the large model processing module includes a large language model.
[0082] An embodiment of the present application also provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any one of the methods for implementing scenario-based robots with different conversation styles.
[0083] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0084] An embodiment of the present application also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is loaded and executed by a processor so that the computer-readable storage medium implements any one of the methods for implementing scenario-based robots with different conversation styles.
[0085] In the above technical solution, the information fusion module is used to fuse the character setting and the script to generate prompt words; the dialogue input module is used to input the dialogue content; the large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue; the versatility is strong and can be applied to multiple different dialogue scenarios, whether in professional fields such as finance, education, and medical care, or in business scenarios such as customer service and sales, it can quickly generate diversified dialogue styles that meet the requirements of the scene; through the script and role setting method, it can flexibly adapt to the needs of different scenarios and has wide application versatility; the speed is high, and there is no need for complex model training or large-scale data fine-tuning, and the use of prompt word engineering can achieve diversified generation of dialogue styles; the development time is greatly shortened, and a dialogue robot system for different application scenarios can be quickly built, which significantly improves the product development efficiency and market response speed; the accuracy of scenario-based dialogue generation, by embedding the script and role settings into the prompt words of the large language model, the robot can generate dialogue content that is highly consistent with the scene in different scenarios; compared with the existing technology, this technology performs better in serious business scenarios such as sales and customer service, and can generate more professional dialogues that better meet user needs.
[0086] Those skilled in the art will appreciate that the present application may be implemented as a system, method or computer program product.
[0087] Therefore, the present disclosure may be specifically implemented in the following forms, namely: it may be completely hardware, it may be completely software (including firmware, resident software, microcode, etc.), or it may be a combination of hardware and software, generally referred to herein as a "circuit", "module" or "system". In addition, in some embodiments, the present application may also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium may contain computer-readable program code.
[0088] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.
[0089] Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art can change, modify, replace and modify the above embodiments within the scope of the present application. On this basis, a variety of replacements and improvements can be made to the present application, all of which fall within the scope of protection of the present application.
Claims
1. A method for implementing scenario-based robots with different conversation styles, characterized in that: The following steps are involved: Use the information fusion module to fuse the character setting and the script to generate prompt words; Using the dialogue input module to input dialogue content; The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
2. The method for implementing scenario-based robots with different conversation styles according to claim 1, characterized in that: Also includes: Use the character generation module to set the robot's character; The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
3. The method for implementing scenario-based robots with different conversation styles according to claim 2, characterized in that: Also includes: Use the dialogue output module to display the dialogue.
4. The method for implementing scenario-based robots with different conversation styles according to claim 3, characterized in that: The large model processing module includes a large language model.
5. A robot dialogue system based on a large language model, characterized in that: include: Information fusion module, used to integrate character settings and scripts and generate prompt words; A dialogue input module, used for inputting dialogue content; The large model processing module is used to process the prompt words and the dialogue content to generate a reply dialogue.
6. The robot dialogue system based on a large language model according to claim 5, characterized in that: Also includes: The character generation module is used to set the character of the robot; The dialogue scene generation module is used to set the series relationship between sub-dialogue scenes to form a dialogue script.
7. The robot dialogue system based on a large language model according to claim 6, characterized in that: Also includes: The dialogue output module is used to display the dialogue.
8. The robot dialogue system based on a large language model according to claim 7, characterized in that: The large model processing module includes a large language model.
9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor so that the electronic device implements the method for realizing scenario-based robots with different conversation styles as described in any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the computer-readable storage medium implements the method for realizing scenario-based robots with different dialogue styles as described in any one of claims 1 to 4.