313 results about "Dialog system" patented technology

In various examples, systems and methods are disclosed relating to language model jailbreak detection using length-perplexity metrics. A system can identify a prompt for a language model—such as an LLM, VLM, etc.—and generate a perplexity score for the prompt. The system can determine, based at least on the perplexity score and a length of the prompt, that the prompt is indicative of a jailbreak attempt for the large language model. The system can restrict the prompt from input to the large language model—or block an output generated based on the prompt from being shared—responsive to determining that the prompt is indicative of the jailbreak attempt.

An intelligent task-type dialogue method based on a large language model, comprising: acquiring a dialogue with a user; identifying and parsing input information of the user to obtain an identification and parsing result; on the basis of the identification and parsing result, synchronously updating the state of a dialogue state tracker; on the basis of the intelligent guidance of the tracker, entering a proper dialogue scene node; executing a preset Action to complete a specific task; and on the basis of the current dialogue node context and dialogue historical data, generating a feedback message of a robot. In the method, by combining the large language model, rapid adaptation and efficient configuration of a scene can be achieved, and by dynamically loading the configuration information of a specific scene, a round of dialogue process can be efficiently pushed on the basis of preset process logic. Also disclosed are an intelligent task-type dialogue system based on a large language model, an electronic device, and a computer program product.

The invention relates to the technical field of artificial intelligence dialogue systems, in particular to an intention correction and optimization method and system for multi-round dialogue context memory. According to the method, joint semantic coding is carried out on user input and historical dialogues, key semantic elements are extracted to construct an intention evolution relation graph, context consistency verification is carried out on an initial intention recognition result, and an intention correction candidate set is generated when conflicts are detected; and dynamically adjusting the context coding weight of the historical dialogue based on the corrected intention recognition result. According to the method, the intention recognition accuracy and context coherence in multiple rounds of conversations are effectively improved, and the semantic migration risk is reduced.

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

The invention relates to the technical field of government affair entrepreneurship guidance service intelligent dialogue systems, in particular to a dialogue intention recognition and correction method based on a knowledge graph, and the method comprises the steps: obtaining a voice signal and text information inputted by a user, and carrying out the cross-modal fusion processing to generate a semantic vector; carrying out evolution modeling on the dialogue state by adopting a graph neural network, and outputting a dynamic dialogue state graph containing a time sequence relationship between a user entity and a business object; taking the semantic vector and the focus information as query conditions, executing dynamic subgraph sampling from the government affair knowledge graph, and generating an intention correction strategy vector; and finally, utilizing reinforcement learning to generate personalized guide statements and outputting the guide statements to the user. According to the method, through deep coupling of semantic understanding, state tracking, knowledge reasoning and decision optimization, real-time perception and active completion of dynamic updating of hidden nodes and policies of the government affair process are achieved, and the intention recognition accuracy and systematic adaptive capacity are effectively improved.

The invention relates to the technical field of intelligent customer service, and discloses an intelligent customer service automatic answering method and system. The method comprises the following steps: constructing a dialogue semantic vector according to a question input by a user and a historical dialogue context; calculating an activation probability corresponding to each field sub-expert model based on the dialogue semantic vector; determining a first core expert model and a second core expert model by using the activation probability, and calculating a corresponding first core expert weight and a corresponding second core expert weight; according to the first core expert weight and the second core expert weight, utilizing a first core expert model and a second core expert model to carry out collaborative reasoning on the dialogue semantic vector to obtain a fusion feature vector; according to the method, the continuity and semantic comprehension accuracy of multiple rounds of dialogues are improved, the expert scheduling accuracy and efficiency are improved, and then the accuracy and integrity of a dialogue system in problem processing in the professional field are improved.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

The invention provides a retrieval enhancement generation method and system in the dual-carbon field, and relates to the field of data processing. According to the method, multi-source unstructured data in the dual-carbon field is collected, after data preprocessing is carried out, a multi-granularity query problem set is formed, a dual-carbon field knowledge base is obtained, and a dual-carbon field-oriented embedding model CEMBING and a reordering model CReranker are constructed. The CEMBING model adopts a semantic partitioning method and a joint training strategy, so that semantic information of a dual-carbon field text can be effectively captured; the CReranker model adopts a negative example mining strategy and a triple loss function, candidate documents can be accurately sorted, the problems of knowledge limitation and insufficient timeliness of LLMs in the application of the dialogue system in the dual-carbon field are effectively solved, and the accuracy and efficiency of retrieval enhancement generation of the dialogue system in the dual-carbon field are improved.

To solve the problem that it is general to generate a response on the basis of single input and an ability for continuously tracking emotions and intentions of a user is limited, and thus, it is difficult to maintain a natural dialog in a conventional AI dialog system.SOLUTION: The present invention applies a technique named dead-reckoning for estimating a current position from a position and speed in the past in navigation and aviation to a dialog with AI, extracts important information from dialog data in the past of a user, and estimates current needs and emotions. Thus, the intentions of the user are understood, directivity of the dialog is defined, the emotions and degrees of interest are grasped as speed and force of the dialog, a psychological state of the user and context of the dialog are more accurately analyzed, and a response adaptable to the analyzed psychological state and context are generated. For that purpose, the emotions of the user are estimated, and directivity of the dialog is analyzed to generate a personalized response by a response generation unit to be constituted of a user emotion analysis unit, a dynamic dialog navigation analysis unit, a personalized response generation unit, and a learning and evolution unit.SELECTED DRAWING: Figure 3

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention.

The invention discloses a front-end cache management method, system and device for a lightweight large-model dialogue state and a medium, belongs to the technical field of front-end cache management of a large-model dialogue system, and aims at solving the technical problem of how to overcome the defects that in a traditional scheme, long context cache is low in efficiency, storage redundancy and insufficient in dynamic semantic adaptation capacity, and the large-model dialogue state cannot be managed easily. In order to realize dialogue context volume compression, improve semantic similar request hit rate and reduce cross-end synchronization delay, the adopted technical scheme is as follows: data acquisition and preprocessing: capturing user interaction behaviors in real time through front-end burying points, and performing preprocessing operation on the acquired user behavior data; semantic normalization processing: performing embedded vector conversion and semantic clustering on the text input by the user to generate a unique semantic identifier and a context vector; querying and updating the multi-level cache; and dynamic collaborative updating: dynamically adjusting the cache based on the cache hit rate, the response delay and the user feedback, and optimizing the cache effect in real time.

A system uses a machine learning based model such as a neural network for transcribing audio inputs. The system receives a set of audio inputs representing utterances of a conversation. For each conversation, the system determines a dialogue state for each utterance. The system uses a hierarchical language model for transcribing audio inputs of an online conversation using the received conversations. The hierarchical language model includes a top-level language model and a plurality of lower-level language model. The training is performed by (1) training the top-level language model using sequences of corresponding dialogue state, each sequence of dialogue states for a conversation, and (2) for each dialogue state, training a lower-level language model using utterances having that dialogue state. The system executes the hierarchical language model to transcribe audio input of new conversations.

The invention discloses a multi-round dialogue system and method based on conversion from a natural language to an SQL, and relates to the technical field of artificial intelligence, and the system comprises a user interaction module which is used for supporting a user to input a query problem and displaying a corresponding SQL query result; the context processing module is used for screening the effective dialogue history of the current question and re-integrating the effective dialogue history into a complete dialogue; the ambiguity processing module is used for identifying an entity from a dialogue history to define a user intention, processing a fuzzy keyword in a current question and guiding the user to complement a query condition; the re-splicing module is used for reconstructing a complete dialogue according to a time sequence, eliminating ambiguity, generating a summary through a large model, and combining the summary with a current problem to construct a complete problem; the SQL generation module is used for performing semantic analysis on the complete problem and outputting an SQL query instruction with the highest matching degree; and the execution and rendering module is used for executing the instruction on the target database and returning a query result. The conversation processing capability can be improved.

The invention relates to the field of man-machine interaction, and discloses a man-machine interaction method and system based on a large language model, and the method comprises the steps: extracting original semantic features from the current input of a user; obtaining a context representation of the current dialogue; extracting a historical emotional state sequence from the multi-round dialogue historical record; generating a current user emotion representation vector based on the context representation of the current dialogue and the historical emotion state sequence; constructing a hierarchical memory structure based on the current user emotion representation vector and the context representation of the current dialogue; adjusting the attention degree of the historical dialogue content stored in the semantic memory component according to the hierarchical memory structure; and generating a response sequence through a large language model based on the adjusted attention degree, the hierarchical memory structure and the context representation of the current conversation, and outputting the response sequence to the user. According to the technical scheme, the problems of stiff switching and dialogue breakage during emotion turning of a traditional dialogue system are solved.

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention. [FIG. 14(a)]

A dialogue system, comprising:an input, configured to receive input data from a user, wherein the input data comprises one or more of text data, speech data, image data and motion data; an output, configured to output data to the user; and one or more processors, configured to:obtain information identifying a skill and obtain information identifying a proficiency level of the user for the identified skill from stored proficiency level information; and execute at least one iteration of a coaching session, each iteration comprising performing one or more dialogue interactions, wherein each dialogue interaction comprises:receiving first input data from the user via the input;generating a first language model prompt and providing the first language model prompt to a language model, said first language model prompt comprising the first input data, the information identifying a skill, the information identifying a proficiency level of the user for the identified skill and a request to generate coaching information based on the first input data, the information identifying a skill and the information identifying a proficiency level; and generating first output data based on a first language model response to the first language model prompt and outputting, via the output, the first output data to the user;wherein the at least one iteration of the coaching session further comprises, after the one or more dialogue interactions:generating a second language model prompt and providing the second language model prompt to the language model, said second language model prompt comprising the information identifying a skill, the information identifying a proficiency level of the user for the identified skill, the first input data and the first output data, and a request to generate at least one proficiency update assessment based on the first input data, the first output data, the identified skill and the information identifying a proficiency level; generating second output data based on a second language model response to the second language model prompt and outputting, via the output, the second output data to the user; receiving second input data from the user via the input; determining a revised proficiency level of the user for the identified skill based on the second input data; andupdating the stored proficiency level information based on the revised proficiency level.

A computer-implemented method of controlling an output from a dialogue system, the method comprising:receiving, by way of an input, first input data relating to speech or text provided by a user;selecting a first state from a plurality of states of a deterministic model, at least some states of the plurality of states being associated with a corresponding portion of a language model prompt including an instruction to call a corresponding function;responsive to the first state being associated with a corresponding portion, generating a first language model prompt comprising at least part of the corresponding portion associated with the selected first state;providing the first language model prompt as input to a language model to generate a first language model output;determining whether to execute a function based on the first language model output;responsive to determining to execute a first function based on the first language model output, executing the determined first function to generate a first function output;selecting a second state from the plurality of states based on the first function output;responsive to the second state being associated with a corresponding portion of a language model prompt, generating a second language model prompt comprising at least part of the corresponding portion associated with the selected second state;providing the second language model prompt as input to the language model to generate a second language model output;determining whether to provide an output to the user based on the second language model output; andresponsive to determining to provide an output to the user based on the second language model output, outputting, by way of an output, speech or text to the user.

On the basis of the embodiments of the present disclosure, provided are a method and apparatus for dialog interaction, and a device and a storage medium. The method comprises: on the basis of a first user input indicative of a first task, using a first model to generate a request for a target entity, so as to instruct the target entity to provide information related to the first task; on the basis of a response from the target entity to the request, providing an execution result of the first task as a reply to the first user input; and on the basis of the response and the first user input, generating a knowledge record for storage in a knowledge base, wherein the knowledge base is used by the first model. Therefore, a task indicated by a user input can be executed by means of a target entity. An improvement in the dialog interaction capability of a dialog system is facilitated, thereby improving the accuracy and efficiency of the dialog system in providing a reply.

The invention discloses a dialogue system based on multiple agents, which comprises an arbitration module, a car knowledge module, a car skill module and a chat module, and is characterized in that the arbitration module responds to an input current problem arbitration unit to generate cue words according to historical arbitration pairs, and adopts a large model to output a falling-domain arbitration result based on the cue words; calling a preset knowledge model to generate an answer based on the current question in response to the vehicle knowledge falling-domain arbitration result; in response to the vehicle skill falling region arbitration result, a plurality of alternative intentions are output through a preset language model according to the current question, an autonomous judgment result is output according to the alternative intentions and the current question, the autonomous judgment result comprises vehicle skill or chat, and the vehicle skill module extracts a final intention and slot position parameters in response to the autonomous judgment result of the vehicle skill; the chat module generates questions and answers in response to the chat autonomous arbitration result.

The invention provides a virtual user simulation method and system for dialogue testing, and the method comprises the steps: obtaining a plurality of pieces of effective multi-round dialogue data from a to-be-tested system, and carrying out the multi-label labeling, so as to construct a basic sample data set containing user portrait labels, the key sample data set further comprises user emotion tags; constructing a group optimization strategy model, performing cold start training based on the basic sample data set, and performing intensive training by using a multi-dimensional composite reward function based on the key sample data set to obtain a dialogue generation model with an emotion cognition-verbal skill expression decoupling architecture; and taking a historical round of dialogue of the to-be-tested system aiming at the current interaction as input of a dialogue generation model, and generating a next round of user reply consistent with the user emotion and the user portrait. According to the invention, deep, efficient and automatic adversarial testing is carried out on the task-oriented dialogue system, so that system defects which are difficult to find by a traditional testing method are effectively exposed.

The invention discloses an express customer service voice robot multi-round task dialogue system based on a large model, which relates to the technical field of artificial intelligence and natural language processing and comprises a dialogue domain classifier, a long and short term memory module, a sub-agent module, an information source module, a dialogue management module and a large model fine tuning and data support module. The dialogue domain classifier is used for routing user query to the corresponding sub-agent module; and the long-short-term memory module is used for constructing a layered persistent memory mechanism. According to the express customer service voice robot multi-round task dialogue system based on the large model, by introducing the large language model, accurate understanding of fuzzy and spoken expression of a user is achieved, the generalization ability of intention recognition is enhanced, a multi-level memory mechanism is adopted, continuity and stability of a dialogue state are ensured, and the dialogue efficiency is improved. Even if the user jumps or asks a question, the smoothness of the conversation can be kept, and the maintenance cost is greatly reduced.

The invention relates to the technical field of artificial intelligence dialogue systems, and particularly discloses an artificial intelligence dialogue generation method based on natural language processing. According to the method, response certainty or diversity is adaptively adjusted according to a dialogue scene through a dynamic temperature sampling strategy, and historical dialogue key features are screened in combination with a gating attention mechanism to realize accurate semantic fusion; word embedding and primary coding are migrated to terminal equipment to be executed by adopting an edge-cloud collaborative architecture, and are transmitted to a cloud end through feature compression and encryption to complete deep decoding; establishing a dual-channel sensitive word detection mechanism of input regular matching and named entity recognition, and blocking privacy leakage through low-temperature sampling and risk word filtering in an output stage; and generating a four-dimensional metadata label driving decision containing the dialogue behavior type, the emotion polarity, the confidence coefficient and the interpretable vector. The method improves the generation quality in the algorithm layer, optimizes the deployment efficiency in the system layer, enhances the security and interpretability in the application layer, and is suitable for intelligent customer service, virtual assistant and other scenes.

The invention discloses a big language model dialogue system for Token-level resident memory and a use method. The big language model dialogue system comprises an inference engine module, a big language model module, an independent memory model module and an external storage module, relates to the technical field of big language model memory enhancement, after a current dialogue Token sequence is received, whether historical dialogue information is needed or not is judged through a big language model module, and if yes, the historical dialogue information is called from an external storage module through an independent memory model module, so that information displayed by finally responding to the Token sequence is more complete, and user experience is improved. The problems that a model cannot completely retain and utilize long-distance historical information, so that questions cannot be answered and information is forgotten are solved; an external storage module is arranged, and model parameters of an independent memory model module and Token-level data of all historical conversations are resided through a memory mapping technology; and zero occupation of the GPU video memory is realized.

The invention discloses a dialogue model training method, a related method, a device, equipment and a storage medium. Comprising the steps of obtaining target text data matched with a dialogue task; performing subject classification on the target text data to obtain subject fact content corresponding to each dialogue subject under the dialogue task; inputting corresponding prompt information generated according to each subject fact content into the trained generation model for dialogue generation to obtain a plurality of initial dialogue contents; performing content specification processing on each initial dialogue content to obtain a plurality of target dialogue contents; and inputting multiple pieces of target dialogue prompt information generated based on the multiple pieces of target dialogue content into a dialogue model for dialogue task training to obtain a trained dialogue model. According to the method, the dialogue task training is performed on the dialogue model by generating the multiple target dialogue contents matched with the dialogue task, so that the dialogue accuracy of the dialogue model in the dialogue task type is improved. The method can be used for various scenes such as artificial intelligence and task-based dialogue systems.

The invention relates to the technical field of intelligent dialogue management, and discloses an intelligent dialogue management method and system for adaptive reinforcement learning, and the method comprises the steps: obtaining a dialogue sequence in multiple rounds of interaction of a user, extracting context correlation features and user feedback real-time data, and generating an initial dialogue sequence propagation model, constructing a dynamic propagation path and adjusting the weight of the path; adjusting the priority sequence of the dialogue content in combination with real-time feedback; generating a final dialogue sequence propagation scheme according to the optimized propagation path and the priority sequence; through combination of a graph neural network and a context reasoning mechanism, context information can be effectively maintained in multiple rounds of dialogues, the efficiency and accuracy of information transmission are improved, and intelligent response can be performed according to real-time requirements of a user; the technical method can be widely applied to intelligent customer service, virtual assistant and other dialogue systems, and has high intelligence, flexibility and user experience.

The application discloses an interview system construction method based on a large language model multi-agent mechanism and belongs to the field of artificial intelligence dialogue systems.The application implements the method as follows: a seed data set of an interview field prompt-question pair is generated;the seed data set based on ChatGPT is cyclically enhanced to form an updated seed data set;and a training data set is obtained after iteration for n times.The interviewer model is trained so that the interviewer model has an interview question generation capability.A role agent that determines an interview question order is designed.An environment agent that generates a text generation standard is designed.A memory agent that stores questions and answers and makes decisions is designed.The designed interview system is tested.The interview records obtained by the interview system are compared and evaluated to obtain an evaluation result.The interview system uses a large language model and a multi-agent mechanism to realize smooth and natural generation of interview questions, and through the answers of interviewees, interactive generation of more natural and logical interview questions is realized.

The invention relates to a high-concurrency real-time dialogue state tracking and context management method, and belongs to the technical field of natural language processing and human-computer interaction. The method comprises the following steps: firstly, unifying multi-modal input into standardized semantic representation; then fusing the historical context and external knowledge to perform semantic analysis; the state change triggered by analysis is packaged into an event and the event is published to a distributed log; an independent service asynchronous consumption event is used for atomic updating of a global state through version control and an optimistic lock mechanism; the read-write performance is improved through multi-level cache and incremental compression; meanwhile, streaming maintenance is based on a long context of a graph, and resources are optimized through life cycle management. Through unified semantic representation, event-driven state management, long context modeling and optimistic concurrency control, a closed-loop collaborative system is constructed, and the concurrent processing capacity, context coherence and response robustness of a dialogue system are remarkably improved.

The invention discloses a context understanding and memory management system and method in large-model multi-round dialogues. The system comprises a five-layer distributed micro-service technology architecture; wherein the business logic layer is integrated with a hierarchical memory management module, a dynamic context coding module, a semantic association engine module and a personalized adaptation module which cooperate with one another; the hierarchical memory management module is responsible for retrieval, hierarchical storage and dynamic updating of dialogue history; the dynamic context coding module is responsible for generating structured context representation; the semantic association engine module is responsible for constructing a cross-round semantic association network; the personalized adaptation module is responsible for generating personalized candidate responses. The technical problems of context loss, memory attenuation, incoherent semantic understanding, insufficient personalized adaptation, low resource utilization rate and the like of an existing large-model multi-round dialogue system are solved, and logic consistency guarantee, memory durability maintenance and personalized experience improvement in a long dialogue scene are realized.

The invention relates to the technical field of computer graphics and human-computer interaction, in particular to an immersive literature interaction system based on WebGL and 3DGS, which comprises a scene reconstruction and data processing module based on 3DGS, a 3DGS real-time rendering engine based on WebGL and an interaction narration module fused with a 3DGS scene. The 3D GS-based scene reconstruction and data processing module is used for reconstructing a high-precision 3D scene from multi-source data and executing lightweight optimization; the WebGL-based 3DGS real-time rendering engine is used for loading and rendering a 3D Gaussian splash model at a browser end, supporting dynamic illumination, texture and viewpoint switching and realizing low-delay interaction; and the interactive narrative module fusing the 3DGS scene associates literature content through AI driving logic, triggers scene visualization response and integrates a role dialogue system to realize immersive narrative experience. The module realizes seamless fusion of literature interaction logic and a 3DGS scene.

The application relates to the technical fields of artificial intelligence and man-machine natural language dialogue, and provides a general memory management method and system based on a language model, which extracts memory information needing to be memorized from dialogue content with the language model, generates temporary memory, integrates the temporary memory to obtain persistent memory about a dialogue user, and is integrated into a dialogue process of the dialogue user and the language model to form memory of the dialogue user, so that the dialogue of the dialogue system is controlled, memory type classification management is performed according to the importance of the memory information in the persistent memory, so that the persistent memory contains short-term memory and long-term memory, the importance of the memory information in the persistent memory is updated and managed, and the short-term memory and the long-term memory are forgotten and upgraded, so that flexible and efficient storage and calling of user memory independent of the model type are realized, and the humanized, continuous and personalized dialogue experience in the man-machine dialogue process is improved.