Multi-model Batch Testing

  • Release version: Zurich
  • Updated July 31, 2025
  • 2 minutes to read
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    Summary of Multi-model Batch Testing

    Multi-model Batch Testing enables ServiceNow customers to evaluate the performance of multiple Natural Language Understanding (NLU) models simultaneously by testing them against large sets of utterances. This feature helps in assessing how well the models predict user intents across supported NLU languages and is part of the NLU Workbench - Advanced Features application.

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    Key Features

    • Test Set Creation: Allows uploading test sets in CSV or XLSX format containing up to 10,000 utterances paired with expected intents. Test sets should represent utterances users are likely to provide and be in the same language as the models.
    • Intent Coverage: Test sets should cover at least 60% of the model’s intents for optimal results. Including utterances with no expected intents helps evaluate the model’s ability to identify irrelevant inputs.
    • Testing Multiple Models: Supports running batch tests across multiple trained NLU models to compare their performance on the same set of utterances.
    • Detailed Test Results: Provides a comprehensive results page showing models tested, utterance counts, prediction accuracy, and a graphical summary of outcomes.
    • Intent Analysis: Highlights top missed or incorrect intents to focus improvement efforts. Users can drill down into specific utterances for detailed insights.
    • Filtering and Exporting: Offers filtering tools on detailed results and the ability to export test outcomes to CSV for further analysis or reporting.
    • Installation Requirement: Requires activation of the NLU Workbench - Advanced Features plugin on the ServiceNow instance.

    Key Outcomes

    By using Multi-model Batch Testing, ServiceNow customers can efficiently validate and compare the accuracy of multiple NLU models, identify areas where models fail to predict correct intents, and improve model precision based on detailed test insights. This process ensures that deployed NLU models better understand user utterances in real-world scenarios, improving conversational AI effectiveness and user satisfaction.

    Test multiple Natural Language Understanding (NLU) models against a large set of utterances to evaluate the performance of the models. Add test sets, test multiple models, and see test results.

    Summary usage

    Use Multi-model Batch Testing to create and upload test sets comprised of utterances and their expected intents. You can then run tests against your NLU models.

    Multi-model Batch Testing works with models for all supported NLU languages. See NLU language support.

    Installation

    Multi-model Batch Testing is part of the NLU Workbench - Advanced Features app available on the ServiceNow® Store.

    To use Multi-model Batch Testing, ensure that the NLU Workbench - Advanced Features (com.snc.nlu.workbench.advanced) plugin is active on your instance. For more information, see Install NLU Workbench - Advanced Features and Activate the NLU Workbench.

    Test sets

    Test sets are lists of utterances and matched intents. Create a test set by using a table in a CSV or XLSX (Excel workbook) file. The table should contain two columns: one for utterances, and one for the expected intent. Your test set can include up to 10,000 rows.

    To get the most out of testing your NLU models, your test sets should include utterances that the model is likely to encounter from your users. Test utterances should be in the same language as the model to be tested. The test set should also include utterances with no expected intents. Including utterances with no expected intent helps assess your model's ability to detect utterances which are irrelevant and shouldn't have any intent predicted.

    By including these types of utterances, the test better assesses the model's ability to perceive intents and respond to your users. If your test set does not cover at least 60% of the intents of the models, you can still run the test but the recommended threshold may not be optimal.
    Note:
    Certain test utterances are skipped during the test if their expected intent does not match any intents in the models.

    To create a test set, see Create a test set.

    After you have a test set, you can test trained NLU models. To begin testing, see Run a multi-model batch test.

    After running a test, your results appear on the Test results page.

    Test results

    The Test results page lists your completed and in-progress tests. At a glance, the results page shows the models tested against, the number of utterances, and prediction percentages.

    Multi-model Batch Testing page with completed tests.

    To see the details of a test result, click the name of the test set.

    The Overview page shows summary information about the results and includes a graphic with a breakdown of predictions.

    The Intents that need attention (Current model) shows the top 5 missed and incorrect intents. Click the intent name to drill down into the test utterances that were predicted incorrectly. Use this information to improve the model.

    The Detailed results tab lists information about each utterance that was tested. From here, you can see the prediction outcome and confidence per model for each utterance. Filter the results by using the search bar or interacting with the filter tools and column headers.

    You can also export the test results to a CSV file by clicking Export. The file includes the same columns as the detailed results page.

    For more information on understanding your test results, see Test and publish your model.