11 results about "Expert opinion" patented technology

The invention discloses a heuristic medical education large model fine tuning method and device based on clinical thinking. The method comprises the step of constructing a clinical thinking framework based on clinical teaching data and expert opinions. According to the heuristic medical education large model fine tuning method and device based on clinical thinking, a structured clinical teaching corpus is formed through a clinical thinking framework, a three-agent collaborative architecture of a teacher agent, an expert agent and a student agent is constructed based on the clinical teaching corpus, and a final data set is generated through the collaborative architecture. And training and fine tuning are carried out through the final data set to obtain the final medical education large model, so that the interactivity, adaptability and teaching guidance capability of the final medical education large model are remarkably improved. The final medical education large model can gradually guide students to conduct reasoning and exploration according to a clinical thinking framework, while the teaching logicality is kept, the independent thinking ability of users is effectively stimulated, moderate guidance and feedback are provided, and therefore the actual requirements of clinical teaching are met.

Methods and systems are described for facilitating knowledge sharing between one or more startups, one or more experts, and one or more investors, and optimizing investment decisions. Embodiments can combine AI / ML with expert opinion in target domains. Embodiments can combine advanced AI tools, including Recommender Systems and Large Language Models, with the expertise of domain specialists and the financial acumen of seasoned investors. Embodiments can streamline the evaluation of investment opportunities, enhances decision-making precision, and aligns investments with market trends and viability. Embodiments can increase accuracy, scalability, and speed of investment decisions by blending AI and collective human perspectives.

To provide a system.SOLUTION: A system comprises: means for using generative AI for forming an expert group in which pieces of knowledge of respective particular fields such as lax, tax and engineering, are combined; means for instantly acquiring a multilateral expert opinion in development of merchandise; and means for contributing to information collection and improvement of accuracy of decision making when performing critical determination, through a unique AI expert network.SELECTED DRAWING: Figure 1

This invention discloses a method and apparatus for fine-tuning a large-scale medical education model based on clinical thinking, comprising constructing a clinical thinking framework based on clinical teaching data and expert opinions. This method and apparatus forms a structured clinical teaching corpus through the clinical thinking framework, and constructs a three-agent collaborative architecture—teacher agent, expert agent, and student agent—based on the corpus. The final dataset is generated through this collaborative architecture, and fine-tuned using the final dataset to obtain the final large-scale medical education model. This significantly improves the interactivity, adaptability, and teaching guidance capabilities of the final large-scale medical education model. The final model can gradually guide students to reason and explore based on the clinical thinking framework, effectively stimulating users' independent thinking ability while maintaining the logical flow of teaching, providing appropriate guidance and feedback, thus meeting the actual needs of clinical teaching.

The invention relates to a multivariate evidence correlation analysis method in the judicial field, and belongs to the technical field of intelligent processing of documents. The method comprises the following steps: loading judicial case file data and extracting an evidence content text; constructing an evidence rule template according to judicial domain knowledge and expert opinions; extracting evidence elements based on the rule template and constructing an evidence library; building a multivariate evidence information network, calculating the condition probability of association information among evidence elements based on a Bayesian network, and forming a multivariate evidence association analysis model; optimizing the multivariate evidence information network through a random strategy reinforcement learning algorithm; and calculating an information probability value of the multivariate evidence subnet, and selecting the most valuable multivariate evidence information network. According to the method, a multivariate evidence correlation analysis event of a judicial case is converted into an updating and optimizing task of a multivariate evidence information network, the complex correlation problem among multivariate evidences is simplified, a correlation model among the multivariate evidences is given, and certain auxiliary support is provided in a judicial decision support system.

The application provides a large language model legal field robustness evaluation method based on knowledge injection attack, wherein the method comprises the following steps: by searching for enhanced generation attack and similar charge attack, the large language model in the legal field is disturbed based on the large premise judgment of the input legal provisions; by vocabulary attack, element attack and narrative attack, the narrative accuracy of the large language model based on the input case facts is disturbed; by previous behavior attack and expert opinion attack, the final conclusion judgment of the large language model is disturbed to evaluate its anti-interference ability in legal reasoning. The application provides a logic attack framework based on Aristotle syllogism, attacks the semantic logic and professional logic reasoning of the problem, more widely covers the error types frequently occurring in real life, and can truly evaluate the robustness of the large model in the field.

The invention provides a knowledge injection attack-based big language model legal field robustness evaluation method, which comprises the following steps of: interfering major premise judgment made by a big language model in the legal field based on input legal provisions by retrieving an enhanced generation attack and a similar criminal name attack; through vocabulary attacks, essential element attacks and narrative attacks, the narrative accuracy of the large language model based on the input case facts is interfered; the final conclusion judgment of the large language model is interfered through previous behavior attacks and expert opinion attacks, so that the anti-interference capability of the large language model in legal reasoning is evaluated. According to the method, a logic attack framework based on the Aristod three-segment theory is provided, semantic logic and professional logical reasoning of the problem are attacked, error types frequently occurring in real life are covered more widely, and the robustness of a large model in the field can be truly evaluated.

Peer review is a project evaluation model commonly used in various professional fields. Multiple experts participating in the same project will score relevant indicators according to their own opinions during the review, and then annotate relevant text opinions. The manually integrated expert group opinions are affected by multiple human factors to a certain extent, and have low timeliness and a large review workload, making it difficult to adapt to the working mode of multi-project online review. This achievement proposes to integrate opinions at the text layer based on knowledge fusion and support the integration of text layer opinions with cognitive layer knowledge processing, which effectively improves the efficiency of manual integration and the degree of knowledge coverage. Supported by the knowledge supplement and knowledge representation methods of creating domain knowledge concept trees and project knowledge ontology, expert opinion knowledge is obtained through SAO structured knowledge acquisition and customized unstructured knowledge acquisition methods, and knowledge association and knowledge fusion are completed on the basis of project knowledge ontology, fully realizing the machine integration of expert group opinions of multi-expert review opinions.

Methods and systems are described for facilitating knowledge sharing between one or more startups, one or more experts, and one or more investors, and optimizing investment decisions. Embodiments can combine AI / ML with expert opinion in target domains. Embodiments can combine advanced Al tools, including Recommender Systems and Large Language Models, with the expertise of domain specialists and the financial acumen of seasoned investors. Embodiments can streamline the evaluation of investment opportunities, enhances decision-making precision, and aligns investments with market trends and viability. Embodiments can increase accuracy, scalability, and speed of investment decisions by blending Al and collective human perspectives.

The invention particularly relates to a false news detection method based on large language model cognition enhancement, which comprises the following steps of: constructing a user portrait model and a personality characteristic model by a cognition-based comment generation module, interacting the user portrait model and the personality characteristic model with a large language model, and generating a personification comment conforming to a user cognition mode; the multi-view comment learning module is used for extracting semantic, emotion and style characteristics from the generated anthropomorphic comments, analyzing the consistency and inconsistency of the comments and news contents based on a large language model, and generating a comment summary; the expert intelligent automatic screening module is used for guiding a large language model to generate expert opinions matched with the news samples through an experience matching strategy and a domain enhancement strategy; and the interactive fusion and classification module fuses the news content, the comment summary and the expert opinions, and outputs the news authenticity probability through a cross attention mechanism and a classifier. Through the design of a plurality of modules, the accuracy and robustness of false news detection are improved.

The invention discloses a process evaluation method based on HDBSCAN clustering and a large language model, and the method comprises the steps: decomposing each expert questionnaire into the data of each evaluation index according to the characteristics of a project and the opinions of experts: the first questionnaire and the first evaluation index of the first questionnaire; the feature vector of each evaluation dimension of each evaluation index is constructed, the evaluation dimension is the first evaluation dimension of the first evaluation index of the first questionnaire, and the evaluation dimensions comprise the expert working age, the traditional process score and the intelligent construction process score. Complicated descriptive texts and a large number of expert questionnaire tables can be integrated to generate scientific and comprehensive process evaluation results and optimization suggestions, the logical reasoning ability of LLM on specific scene complex corpora is enhanced, and the LLM input length limitation is overcome.