{"id":934245,"date":"2023-04-11T09:42:51","date_gmt":"2023-04-11T16:42:51","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2025-06-19T07:03:44","modified_gmt":"2025-06-19T14:03:44","slug":"what-it-wants-me-to-say-bridging-the-abstraction-gap-between-end-user-programmers-and-code-generating-large-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/what-it-wants-me-to-say-bridging-the-abstraction-gap-between-end-user-programmers-and-code-generating-large-language-models\/","title":{"rendered":"&#8220;What It Wants Me To Say&#8221;: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models"},"content":{"rendered":"<div>\n<div>Code-generating large language models map natural language to code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding code generation. For non-expert end-user programmers, learning this is the challenge of <em>abstraction matching<\/em>. We examine this challenge in the specific context of data analysis in spreadsheets, in a system that maps the user&#8217;s natural language query to Python code using the Codex generator, executes the code, and shows the result. We propose <em>grounded<\/em> abstraction matching, which bridges the abstraction gap by translating the code back into a systematic and predictable naturalistic utterance. In a between-subjects, think-aloud study (n=24), we compare grounded abstraction matching to an ungrounded alternative based on previously established query framing principles. We find that the grounded approach improves end-users&#8217; understanding of the scope and capabilities of the code-generating model, and the kind of language needed to use it effectively.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Code-generating large language models map natural language to code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding code generation. For non-expert end-user programmers, learning this is the challenge of abstraction matching. We examine this challenge in the specific context of data analysis in spreadsheets, in a system [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the ACM CHI Conference on Human Factors in Computing 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