A comprehensive evaluation test method and system based on large model understanding ability

Abstract

The application discloses a comprehensive evaluation test method and system based on large model understanding ability, and relates to the technical field of computers. First, rich and varied texts and multi-modal data are acquired as a test set, the text and multi-modal associated understanding are taken into account, and the large model capability is comprehensively investigated. Second, preset comprehensive and detailed evaluation indexes can determine the advantages and disadvantages of the model and propose optimization suggestions, thereby helping targeted optimization. Third, the test data set is regularly updated and expanded, the timeliness and comprehensiveness of the evaluation are maintained, the evaluation result is more in line with actual application requirements, and the problems that the evaluation data set of the large model test method in the prior art is not rich in theme, type and difficulty, the evaluation method mainly focuses on text understanding and ignores multi-modal association, the evaluation indexes are not comprehensive and detailed enough, and there is a lack of optimization suggestions for the advantages and disadvantages of the model after the evaluation, resulting in one-sided and inaccurate evaluation results, which is not conducive to model improvement.

Description

Technical Field

[0001] This invention relates to the field of computer technology, specifically to a comprehensive evaluation and testing method and system based on large model understanding ability. Background Technology

[0002] Large-scale models, by learning from massive amounts of data, can adapt to various tasks and scenarios, demonstrating strong generalization capabilities. For example, a large language model trained on a large amount of text data can be used for various natural language processing tasks such as translation, question answering, and text generation.

[0003] In existing technologies, the evaluation of the understanding ability of large models often suffers from the following drawbacks: First, the evaluation datasets are not rich enough in terms of topics, types, and difficulty, which makes it impossible to fully examine the understanding ability of large models in different scenarios, resulting in one-sided evaluation results.

[0004] Second, many evaluation methods focus only on text understanding and ignore the relationship between multimodal data and text understanding, thus failing to accurately assess the comprehensive understanding ability of large models in practical applications.

[0005] Third, the evaluation metrics are not comprehensive and detailed enough to accurately quantify the performance of large models in different tasks and make it difficult to discover potential problems with the models.

[0006] Fourth, evaluations often only provide the results without specific optimization suggestions regarding the model's strengths and weaknesses, which hinders model improvement. To address these shortcomings, this invention provides a comprehensive evaluation and testing method and system based on large model understanding capabilities to solve the aforementioned problems. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a comprehensive evaluation and testing method and system based on large model understanding capabilities. First, it acquires a rich and diverse set of text and multimodal data as test data, taking into account both textual and multimodal relational understanding to comprehensively examine the capabilities of large models. Second, it pre-sets comprehensive and detailed evaluation indicators to identify model strengths and weaknesses and propose optimization suggestions, facilitating targeted optimization. Third, it regularly updates and expands the test dataset to maintain the timeliness and comprehensiveness of the evaluation, ensuring that the evaluation results better align with practical application needs.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive evaluation and testing method based on large model understanding ability, comprising the following steps: Step S1: Obtain text data and multimodal data containing different topics, types and difficulties as test datasets; Step S2: Preprocess the test dataset to extract key semantic features of the text data and correlation features of the multimodal data; Step S3: Input the preprocessed data into the large model to be tested and record the output results of the large model to be tested; Step S4: Evaluate the output of the large model under test according to the preset evaluation index to obtain the performance of the large model under test in text understanding and multimodal understanding tasks. Step S5: Based on the performance, determine the advantages and disadvantages of the large model under test.

[0009] Preferably, the preprocessing of the test dataset includes: Cleaning, labeling, and classifying text data; Feature extraction and data format standardization are performed on multimodal data.

[0010] Preferably, before inputting the preprocessed data into the large model to be tested, the method further includes feature fusion of the preprocessed data to generate a fused feature vector.

[0011] Preferably, the output results of the large model under test are evaluated according to preset evaluation indicators, including: Calculate the accuracy, recall, and F1 score of the large model under test in the text understanding task; Calculate the semantic consistency, information fusion accuracy, and cross-modal relevance of the large model under test in the multimodal understanding task.

[0012] Preferably, after determining the advantages and disadvantages of the large model under test based on the performance, optimization suggestions for the large model under test are generated based on the advantages and disadvantages.

[0013] Preferably, after generating optimization suggestions for the large model under test based on the advantages and disadvantages, the method further includes: Based on the optimization suggestions, the large model to be tested was optimized and adjusted. Repeat the step of inputting the preprocessed data into the large model under test to verify the optimization effect.

[0014] Preferably, the evaluation results of the large model under test are compared and analyzed with preset performance benchmarks and evaluation results of other large models to determine the advantages and disadvantages of the large model under test in text understanding and multimodal understanding.

[0015] Preferably, a detailed test report is generated based on all evaluation results. The test report includes text understanding ability, multimodal understanding ability, performance comparison charts, and model optimization suggestions.

[0016] Preferably, the test dataset is updated and expanded regularly, introducing new text data and multimodal data to maintain the timeliness and comprehensiveness of the evaluation.

[0017] A second aspect of the present invention provides a comprehensive evaluation testing system based on large model understanding ability, applied to a comprehensive evaluation testing method based on large model understanding ability, the testing system comprising: The data management module is used to acquire, store, and manage text data and multimodal data; The feature processing module is used to preprocess, extract, and fuse the text data and multimodal data; The evaluation execution module is used to input the processed data into the large model under test and record the output results of the large model under test; The results analysis module is used to evaluate the output results of the large model under test according to preset evaluation indicators, and to determine the advantages and disadvantages of the large model under test. The optimization suggestion module is used to generate optimization suggestions for the large model under test based on the advantages and disadvantages mentioned above. The system configuration module is used to configure evaluation parameters, task types, and dataset selection.

[0018] This invention discloses a comprehensive evaluation and testing method and system based on large model understanding capabilities, which has the following beneficial effects: 1. This comprehensive evaluation and testing method based on large model understanding capabilities ensures the comprehensiveness and diversity of the evaluation by acquiring text data and multimodal data containing different topics, types, and difficulties as test datasets. This avoids the problem of insufficient topic, type, and difficulty richness in the evaluation datasets of existing technologies, and can comprehensively examine the understanding capabilities of large models in different scenarios. Furthermore, this invention not only focuses on text understanding but also fully considers the correlation between multimodal data and text understanding. By extracting the correlation features of multimodal data and fusing them with features from other modalities, this invention can accurately evaluate the comprehensive understanding capabilities of large models in practical applications.

[0019] 2. This invention provides a comprehensive evaluation and testing method based on large-scale model understanding capabilities. It pre-defines comprehensive and detailed evaluation metrics to quantify the performance of the large-scale model under test in text understanding and multimodal understanding tasks. Furthermore, based on the evaluation results, this invention can identify the strengths and weaknesses of the large-scale model under test and propose specific optimization suggestions. This facilitates targeted model optimization and improves model performance.

[0020] 3. This comprehensive evaluation and testing method based on large model understanding capabilities regularly introduces new text data and multimodal data to update and expand the test dataset as technology develops and application scenarios change, thereby maintaining the timeliness and comprehensiveness of the evaluation and making the evaluation results more in line with actual application needs. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the overall method of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This application provides a comprehensive evaluation and testing method and system based on large model understanding capabilities. It solves the problems of existing large model testing methods, such as insufficient richness of evaluation datasets in terms of topics, types and difficulties, evaluation methods focusing only on text understanding while ignoring multimodal associations, insufficiently comprehensive and detailed evaluation indicators, and lack of optimization suggestions for the model's strengths and weaknesses after evaluation, resulting in one-sided and inaccurate evaluation results that are not conducive to model improvement.

[0025] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0026] Example 1: This invention discloses a comprehensive evaluation and testing method based on large model understanding ability, according to the appendix. Figure 1 As shown, it includes the following steps: Step S1: Obtain text data and multimodal data containing different themes, types, and difficulties as the test dataset; collect text data and multimodal data of different themes, types, and difficulties from multiple sources. Text data can cover different themes such as news, academic papers, novels, and social media posts; types include plain text and formatted text; difficulty is measured by the complexity of the text, the frequency of use of technical terms, etc. Multimodal data can include image-text pairs, audio-text pairs, video-text pairs, etc. Images involve different themes such as natural scenery, people, and products; audio includes speech and music; and videos include documentaries and film clips.

[0027] When acquiring test datasets, determine the evaluation objectives. For example, to evaluate the understanding capabilities of large models in the medical field, focus on collecting medical-related text and multimodal data.

[0028] Partner with data providers or utilize public dataset platforms to obtain the data you need.

[0029] The collected data is initially screened to remove data that is clearly unsuitable.

[0030] Diverse test datasets can comprehensively examine the understanding ability of large models in different scenarios, avoiding biased evaluation results due to limited data.

[0031] Step S2 involves preprocessing the test dataset to extract key semantic features from the text data and association features from the multimodal data. The preprocessing of the test dataset includes both text data preprocessing and multimodal data preprocessing. In text data preprocessing, cleaning is performed to remove noise from the text, such as special characters and garbled text; key information is labeled, such as entities in the text; and the text is classified according to topic or type to facilitate subsequent processing and analysis.

[0032] In multimodal data preprocessing, features such as color, texture, and shape are extracted from image data; features such as frequency, pitch, and rhythm are extracted from audio data; and keyframes are extracted from video data first, followed by image feature extraction from the keyframes. Simultaneously, multimodal data of different formats are standardized, for example, images of different resolutions are resized to a uniform size, and audio with different sampling rates is converted to the same sampling rate.

[0033] During preprocessing, a text cleaning script is written to remove noise using tools such as regular expressions.

[0034] Use natural language processing tools such as NLTK for text annotation and classification.

[0035] For data of different modalities, select appropriate feature extraction algorithms, such as SIFT for image feature extraction and MFCC for audio feature extraction, and write code to unify the data format.

[0036] Preprocessing can improve data quality, extract features useful for evaluation, reduce the computational load of subsequent processing, and improve evaluation efficiency.

[0037] Step S3 involves inputting the preprocessed data into the large-scale model under test and recording its output. The key semantic features of the preprocessed text data and the association features of the multimodal data are then fused to generate a fused feature vector. Methods such as concatenation, weighted summation, and deep learning model fusion can be used. Concatenation is simple and direct, connecting different feature vectors sequentially. Weighted summation assigns different weights based on the importance of each feature before adding them together. Deep learning model fusion can learn the complex relationships between features.

[0038] Analyze the importance and correlation of different features to determine the fusion method.

[0039] If a concatenation method is used, write code to connect the feature vectors; if a weighted summation method is used, determine the weights and calculate them; if a deep learning model fusion method is used, design and train the fusion model.

[0040] Feature fusion can comprehensively consider information from different modalities, enabling the large model under test to better understand multimodal data and improve the accuracy of evaluation.

[0041] Set up a suitable operating environment and input the preprocessed and feature-fused data into the large model to be tested. Deploy the large model on a local server or cloud platform. Record the model's output results, including the output text for the text understanding task and the correlation judgments between different modal information in the multimodal understanding task.

[0042] Configure the runtime environment according to the requirements of the large model, and install the corresponding deep learning frameworks, including TensorFlow and PyTorch.

[0043] Write a data input script to input data into a large model in batches.

[0044] Design a method for recording output results, such as saving the results to a file or database. Accurately recording output results is the basis for subsequent evaluation and can provide a reliable basis for analyzing the performance of the model.

[0045] Step S4: Evaluate the output of the large model under test according to the preset evaluation index to obtain the performance of the large model under test in text understanding and multimodal understanding tasks. In text understanding task evaluation, accuracy, recall, and F1 score are calculated to measure the model's performance in tasks such as text classification and named entity recognition.

[0046] Accuracy refers to the proportion of correctly predicted samples out of the total number of samples. Recall rate refers to the proportion of samples that were actually positive that were correctly predicted as positive. The F1 score is the harmonic mean of precision and recall.

[0047] In the evaluation of multimodal understanding tasks, semantic consistency is calculated to determine whether the model's understanding of the semantics of multimodal data is consistent; information fusion accuracy is evaluated to assess the model's effectiveness in fusing information from different modalities; and cross-modal correlation is measured to assess the model's grasp of the relationships between different modalities.

[0048] During the evaluation process, code for calculating evaluation metrics was written to calculate various metrics based on the output results and true labels. These specific evaluation metrics enable the quantification of the performance of the large model under test in text understanding and multimodal understanding tasks, facilitating comparisons of the merits of different models.

[0049] Step S5: Based on performance, determine the strengths and weaknesses of the large model under test. Analyze the evaluation metrics to identify where the model performs well and where it is lacking. For example, if the accuracy is high in text classification tasks but the recall is low when processing text with complex semantics, it indicates that the model has an advantage in simple text classification but is insufficient in understanding complex semantics.

[0050] Establish criteria for judging the advantages and disadvantages, such as accuracy exceeding a certain threshold being an advantage and accuracy below a certain threshold being a disadvantage. Based on the evaluation results and the judgment criteria, summarize the advantages and disadvantages of the model.

[0051] Identifying the strengths and weaknesses of a model helps in targeted optimization and improves its performance.

[0052] Based on the model's strengths and weaknesses, specific optimization suggestions are proposed. If the model is insufficient in understanding complex semantics, it is recommended to add text data with complex semantics for training; if the multimodal information fusion effect is poor, it is recommended to improve the feature fusion method or model structure.

[0053] For each drawback, analyze the possible causes. Based on the causes, propose corresponding optimization suggestions and record them. These optimization suggestions provide direction for model improvement and can increase the efficiency of model development.

[0054] Based on the optimization suggestions, the model is modified through code changes, data updates, or model structure adjustments. Then, the process of inputting the preprocessed data into the large model under test is repeated, and the model performance is evaluated again. The evaluation metrics before and after optimization are compared to verify the optimization effect.

[0055] When optimizing and verifying the model, write optimization code and implement optimization suggestions. Rerun the entire evaluation process and record the evaluation results after optimization. Compare the results before and after optimization to determine whether the optimization is effective. By verifying the optimization effect, the effectiveness of the optimization measures can be ensured, and model performance can be continuously improved.

[0056] The evaluation results of the large model under test are compared with the pre-set performance benchmarks and the evaluation results of other large models. The relative position of the large model under test on different tasks and indicators is analyzed to identify its strengths and weaknesses.

[0057] When comparing the performance with preset benchmarks and evaluation results of other large models, collect preset benchmark data and evaluation results of other large models. Write comparative analysis code or use visualization tools to generate comparative charts. Based on the comparison results, summarize the characteristics of the large model under test. Comparative analysis can clarify the position of the large model under test in the industry and provide a reference for further optimization.

[0058] Finally, based on all evaluation results, a detailed test report is generated, including text understanding capabilities, multimodal understanding capabilities, performance comparison charts, and model optimization suggestions. The report can be presented in various formats, such as text descriptions, tables, and charts.

[0059] When generating a detailed test report, design a test report template and determine its content and format. Fill the template with evaluation results, comparative analysis data, optimization suggestions, and other information. Review and revise the report to ensure accuracy and clarity. A detailed test report provides model developers and users with comprehensive information, facilitating understanding of model performance and decision-making.

[0060] As technology advances and application scenarios change, new textual and multimodal data are regularly introduced to update and expand the test dataset. For example, when new disease types emerge, relevant medical text and multimodal data are added.

[0061] Develop a dataset update plan and determine the update frequency. Collect new data according to the plan, and preprocess and label it. Add the new data to the test dataset, replacing or supplementing the old data. Maintain the timeliness and comprehensiveness of the evaluation, ensuring that the evaluation results better reflect practical application needs.

[0062] A second aspect of this invention provides a comprehensive evaluation testing system based on large model understanding ability, applied to a comprehensive evaluation testing method based on large model understanding ability. The testing system includes: The data management module is used to acquire, store, and manage text and multimodal data. It can obtain data from local file systems, databases, web crawlers, and other sources, and store the data in suitable storage media. The data management module provides data query and retrieval functions. Centralized data management facilitates data acquisition and use, and improves data security.

[0063] The feature processing module is used for preprocessing, feature extraction, and fusion of text and multimodal data. It implements functions such as text cleaning, annotation, classification, multimodal data feature extraction and format standardization, and feature fusion of different modalities. The feature processing module provides high-quality feature data to the evaluation execution module, improving the accuracy of the evaluation.

[0064] The evaluation execution module is used to input processed data into the large model under test and record the output results of the large model. The evaluation execution module interacts with the large model to achieve batch data input and accurate recording of results. The evaluation execution module ensures the automation and efficiency of the evaluation process and reduces manual intervention.

[0065] The results analysis module is used to evaluate the output results of the large model under test according to the preset evaluation indicators, and to determine the advantages and disadvantages of the large model under test. The results analysis module calculates various evaluation indicators, analyzes the indicator results, summarizes the advantages and disadvantages of the model, provides a basis for model optimization, and helps developers understand the model performance.

[0066] The optimization suggestion module generates optimization recommendations for the large model under test based on its strengths and weaknesses. It analyzes the reasons for the model's shortcomings and proposes specific optimization measures. This module guides model developers in optimization, improving model development efficiency.

[0067] The system configuration module is used to configure evaluation parameters, task types, and dataset selection. Users can use this module to set evaluation metric weights, select different evaluation tasks such as text classification and image caption generation, and choose appropriate test datasets. This enhances the system's flexibility and adaptability, catering to the needs of diverse users.

[0068] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive evaluation and testing method based on large model comprehension ability, characterized in that, Includes the following steps: Step S1: Obtain text data and multimodal data containing different topics, types and difficulties as test datasets; Step S2: Preprocess the test dataset to extract key semantic features of the text data and correlation features of the multimodal data; Step S3: Input the preprocessed data into the large model to be tested and record the output results of the large model to be tested; Step S4: Evaluate the output of the large model under test according to the preset evaluation index to obtain the performance of the large model under test in text understanding and multimodal understanding tasks. Step S5: Based on the performance, determine the advantages and disadvantages of the large model under test.

2. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, The preprocessing of the test dataset includes: Cleaning, labeling, and classifying text data; Feature extraction and data format standardization are performed on multimodal data.

3. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, Before inputting the preprocessed data into the large model to be tested, the process also includes feature fusion of the preprocessed data to generate a fused feature vector.

4. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, The output results of the large model under test are evaluated according to preset evaluation indicators, including: Calculate the accuracy, recall, and F1 score of the large model under test in the text understanding task; Calculate the semantic consistency, information fusion accuracy, and cross-modal relevance of the large model under test in the multimodal understanding task.

5. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, After determining the advantages and disadvantages of the large model under test based on the performance, optimization suggestions are generated for the large model under test based on the advantages and disadvantages.

6. The comprehensive evaluation and testing method based on large model understanding ability according to claim 5, characterized in that, After generating optimization suggestions for the large model under test based on the aforementioned advantages and disadvantages, the process also includes: Based on the optimization suggestions, the large model to be tested was optimized and adjusted. Repeat the step of inputting the preprocessed data into the large model under test to verify the optimization effect.

7. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, The evaluation results of the large model under test are compared and analyzed with the preset performance benchmark and the evaluation results of other large models to determine the advantages and disadvantages of the large model under test in text understanding and multimodal understanding.

8. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, Based on all evaluation results, a detailed test report is generated, which includes text understanding ability, multimodal understanding ability, performance comparison charts, and model optimization suggestions.

9. The comprehensive evaluation and testing method based on large model understanding ability according to claim 1, characterized in that, The test dataset is regularly updated and expanded, introducing new text and multimodal data to maintain the timeliness and comprehensiveness of the evaluation.

10. A comprehensive evaluation and testing system based on large model understanding ability, applied to the comprehensive evaluation and testing method based on large model understanding ability as described in any one of claims 1-9, characterized in that, The testing system includes: The data management module is used to acquire, store, and manage text data and multimodal data; The feature processing module is used to preprocess, extract, and fuse the text data and multimodal data; The evaluation execution module is used to input the processed data into the large model under test and record the output results of the large model under test; The results analysis module is used to evaluate the output results of the large model under test according to preset evaluation indicators, and to determine the advantages and disadvantages of the large model under test. The optimization suggestion module is used to generate optimization suggestions for the large model under test based on the advantages and disadvantages mentioned above. The system configuration module is used to configure evaluation parameters, task types, and dataset selection.

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