![]() The document contains a prompt details auto text code, which is configured to display the prompt titles and index. Regional Revenue contains the Year and Region attributes, and the Revenue metric. #AUTOPROMPT SKIP PROMPT CODE#For more information on the levels of inheritance, see Levels of auto text code configuration.įor example, a document contains these two dataset reports:Ĭustomers per Employee contains the Region attribute and the metrics Count of Customers, Employee Headcount, and Customers per Employee. If you are configuring all the auto text codes in the document, the setting is inherited from the report setting. If you are configuring the auto text codes in a specific text field, the setting is inherited from the document setting. The resulting report, which you can use as a dataset report, can display or omit the prompt details from the original report (the report that you drilled on).įor the prompt title and attribute name settings, you can also choose to inherit the setting instead. An unused prompt occurs when you drill on a Grid/Graph that contains a prompt. For information on browse forms, see the Project Design Guide. The browse form of the attribute, which is displayed when a user answers the prompt, is used to display the attribute elements in the prompt details auto text code. Repeat the attribute name for each prompt answer (for example, Region = North, Region = South) The options are:ĭisplay the attribute name (for example, Region) #AUTOPROMPT SKIP PROMPT HOW TO#Whether and how to display the attribute name for any attribute element list prompts in the document. An unanswered filter definition prompt displays as "All" because the report is not filtered and therefore all the objects appear on the report. For example, an unanswered object prompt displays as "None", because no objects are selected. Whether the word "All" or "None" displays depends on the type of prompt. The text to display when a prompt is unanswered. Whether the prompt title and index (a number indicating the order of the prompts in the dataset report) are displayed. The report prompt details auto text code displays the prompt information for all prompts in the document. These results demonstrate that automatically generated prompts are a viable parameter-free alternative to existing probing methods, and as pretrained LMs become more sophisticated and capable, potentially a replacement for finetuning.Configuring the report prompt details auto text codeĪuto text codes are document or dataset variable information. ![]() We also show that our prompts elicit more accurate factual knowledge from MLMs than the manually created prompts on the LAMA benchmark, and that MLMs can be used as relation extractors more effectively than supervised relation extraction models. ![]() Using AutoPrompt, we show that masked language models (MLMs) have an inherent capability to perform sentiment analysis and natural language inference without additional parameters or finetuning, sometimes achieving performance on par with recent state-of-the-art supervised models. To address this, we develop AutoPrompt, an automated method to create prompts for a diverse set of tasks, based on a gradient-guided search. #AUTOPROMPT SKIP PROMPT MANUAL#Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prompts. The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. ![]()
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