{"id":932820,"date":"2023-04-04T13:12:04","date_gmt":"2023-04-04T20:12:04","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2024-01-25T08:20:36","modified_gmt":"2024-01-25T16:20:36","slug":"lida-automatic-generation-of-grammar-agnostic-visualizations-and-infographics-using-large-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/lida-automatic-generation-of-grammar-agnostic-visualizations-and-infographics-using-large-language-models\/","title":{"rendered":"LIDA: Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models"},"content":{"rendered":"

Systems that support users in the automatic creation of visualizations must address several subtasks – understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. In this work, we\u00a0pose visualization generation as a multi-stage generation problem\u00a0and argue that well-orchestrated pipelines based on large language models (LLMs) and image generation models (IGMs) are suitable to addressing these tasks. We present LIDA, a novel tool for generating grammar-agnostic visualizations and infographics. LIDA comprises of 4 modules – A SUMMARIZER that converts data into a rich but compact natural language summary, a GOAL EXPLORER that enumerates visualization goals given the data, a VISGENERATOR that generates, refines, executes and filters visualization code and an INFOGRAPHER module that yields data-faithful stylized graphics using IGMs. LIDA provides a python api, and a hybrid user interface (direct manipulation and\u00a0multilingual\u00a0natural language) for interactive chart, infographics and data story generation.<\/p>\n

 <\/p>\n

\"LIDASystem architecture for LIDA<\/span><\/p>\n

 <\/p>\n

 <\/p>\n

\"Example<\/p>\n

Example infographics generated with LIDA<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Systems that support users in the automatic creation of visualizations must address several subtasks – understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. In this work, we\u00a0pose visualization generation as a multi-stage generation problem\u00a0and argue that well-orchestrated pipelines based on large language models (LLMs) and image generation models (IGMs) are […]<\/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":[{"type":"user_nicename","value":"Victor Dibia","user_id":"41311"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"The 61st Annual Meeting of the Association for Computational Linguistics","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"ACL 2023 Demonstrations","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2023-3-6","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"https:\/\/2023.aclweb.org\/","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13554],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[253798,252175],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-932820","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-field-of-study-data-visualization","msr-field-of-study-hci"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2023-3-6","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"The 61st Annual Meeting of the Association for Computational Linguistics","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/abs\/2303.02927","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Victor Dibia","user_id":41311,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Victor Dibia"}],"msr_impact_theme":[],"msr_research_lab":[199565,992148],"msr_event":[945648],"msr_group":[781564],"msr_project":[931371],"publication":[],"video":[],"msr-tool":[966237],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":931371,"post_title":"LIDA: Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models","post_name":"lida-automatic-generation-of-grammar-agnostic-visualizations","post_type":"msr-project","post_date":"2023-03-28 13:43:57","post_modified":"2024-01-25 08:23:15","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/lida-automatic-generation-of-grammar-agnostic-visualizations\/","post_excerpt":"Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models Systems that support users in the automatic creation of visualizations must address several subtasks - understand the semantics of data, enumerate relevant visualization goals and generate visualization specifications. 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