8 results about "Problem statement" patented technology

A multi-agent collaboration tool for collaborating among multiple large language model (LLM) agents is provided. A problem statement is received from a user. A first LLM agent is automatically selected to provide a first answer to the problem statement as a first confidence score for the first LLM agent is more than a second confidence score for a second LLM agent to provide the first answer to the problem statement. The second LLM agent is automatically selected to provide a second answer based on the first answer to the problem statement as a third confidence score for the second LLM agent is more than a fourth confidence score for the first LLM agent to provide the second answer based on the first answer to the problem statement. A solution to the problem statement is provided based on the second answer.

The invention discloses a question clarification method and device based on entity and intention fuzziness analysis, equipment, a medium and a product, and relates to the field of intelligent questions.The method comprises the steps that a question statement input by a user is obtained; cyclically executing the following steps until the entity ambiguity and the intention ambiguity of the problem statement are both smaller than or equal to a preset threshold: analyzing the entity and the intention in the problem statement to obtain the entity ambiguity and the intention ambiguity of the problem statement; under the condition that the entity ambiguity and / or the intention ambiguity are / is greater than a preset threshold value, generating target clarification request content based on entity ambiguity corresponding to the entity ambiguity and / or intention ambiguity corresponding to the intention ambiguity by utilizing a large language model; sending target clarification request content to the user, and obtaining target clarification response content of the user; and on the basis of the target clarification response content, the semantic representation of the question statement is updated, and the semantic ambiguity of the user question can be dynamically quantified by calculating the entity ambiguity and the intention ambiguity.

The invention discloses a large model reasoning method and device, related equipment and a computer program product, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining a problem statement of a to-be-processed problem, then judging whether a current node is a key node or not according to an input token of a current reasoning node in a chain reasoning process of a large model based on the problem statement, and if yes, judging whether the current node is the key node or not; and if not, performing compression processing on the original output token of the previous node, and reasoning subsequent nodes based on a compression processing result. According to the method, whether the reasoning nodes are critical or not is judged, and then the output token of the non-critical reasoning nodes is compressed during subsequent reasoning, so that the input data volume needing to be processed in the subsequent reasoning process is reduced, the subsequent processing speed is increased, and optimization of the calculation efficiency of chain reasoning, particularly long chain reasoning is facilitated.

A device may receive and process client data, with a first machine learning model, to determine current state data identifying a current state of a client. The device may process the current state data and prior client data, with a second machine learning model, to determine a problem statement for the client and future state data of the client. The device may utilize the second machine learning model to identify initiatives for the client, and costs of the initiatives, based on the problem statement, the current state data, and the future state data, and to assign benefits and priorities to the initiatives. The device may process the initiatives, the benefits and priorities of the initiatives, and the costs of the initiatives, with the second machine learning model, to generate an initiative plan for solving a problem of the problem statement, and may perform actions based on the initiative plan.

A method (600) for computer-aided troubleshooting of a packaging line (1) configured to produce packages is disclosed. In one embodiment, the method (600) comprises the steps of: receiving (602), via an input device, a natural language question statement (402) from a user, the question statement indicating questions that have occurred during operation of the packaging line (1); generating (604), by a trained language model (42), one or more instructions (404) based at least in part on the question statement (402), the one or more instructions (404) indicating an operation to be executed by a user on the packaging line (1) to alleviate the question; and outputting (606), by an output device, the one or more instructions (404) in a natural language to a user. A method (500) of training a language model (42), a language model (42), a data processing device (4), a packaging line (1) and a computer program are also disclosed.

The aim is to provide learners with a variety of similar problems while reducing the burden on the system and the question setters. [Solution] A program according to one embodiment of the present invention, which causes one or more computers to function as means for managing problem information including variable part information and format information which is the format of the answer, means for determining the variable part information and generating a problem statement based on the problem information, and means for outputting the generated problem statement, wherein the means for generating the problem statement determines the variable part information and the format information based on random number processing.

The present application discloses a large-model reasoning method, apparatus, related equipment, and computer program product, relating to the field of artificial intelligence. The present application first obtains a problem statement of a problem to be processed, and then, in the process of chain reasoning based on the problem statement in the large model, determines whether the current node is a key node based on the input token of the current reasoning node. If not, the original output token of the previous node is compressed, and the subsequent node is inferred based on the compression processing result. The present application first determines whether the reasoning node is key or not, and then compresses the output token of non-key reasoning nodes when performing subsequent reasoning, thereby reducing the amount of input data required to be processed in the subsequent reasoning process, speeding up the subsequent processing speed, and helping to optimize the computational efficiency of chain reasoning, especially long chain reasoning.

Provide a system. 【Solution means】 Means for receiving user input and analyzing the intention of the question based on the input, Means for referring to past test data and generating problem ideas based on the analysis results, Means for creating a specific problem statement from the generated problem ideas and checking logical consistency, Means for estimating the difficulty level of the problem statement, Means for analyzing the ideal answer and creating scoring criteria, Means for analyzing voice input and converting it into text data, Means for automatically generating problems using a generation AI model, Means for providing feedback to the user based on the evaluation results, A system including the above.