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An MBTI Personality Detection Dataset Aligned with Population Traits","","The Myers-Briggs Type Indicator (MBTI) is a widely used personality framework, yet existing MBTI personality detection datasets are misaligned with population trait distributions. This work introduces MBTIBENCH, a manually annotated high-quality dataset with soft labels guided by psychologists. It addresses incorrect self-reported labeling, which contributes 29.58% of the data, and estimates soft-label polarity tendencies. Results reveal polarized LLM biases and show soft labels benefit psychological tasks beyond hard labels.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/can-large-language-models-understand-you-better-an-mbti-personality-detection-dataset-aligned-with-population-traits/293377/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/can-large-language-models-understand-you-better-an-mbti-personality-detection-dataset-aligned-with-population-traits/293377.png","ImageObject",300,407,{"name":92,"@type":93},"Dozel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does MBTIBENCH address in existing MBTI personality detection datasets?","Question",{"text":113,"@type":114},"It targets two limitations: incorrect labels arising from self-reported MBTI types and the lack of soft labels that fail to reflect the full population trait spectrum.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does MBTIBENCH handle label noise from self-reported MBTI types?",{"text":118,"@type":114},"It proposes data filtering guidelines for MBTI detection and manually re-annotates the cleaned datasets with labels that best describe each post’s personality polarity.",{"name":120,"@type":111,"acceptedAnswer":121},"What are soft labels in MBTIBENCH and why are they important?",{"text":122,"@type":114},"Soft labels are estimated by deriving the polarity tendency of samples, allowing the dataset to represent non-extreme personality traits rather than only binary categories.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},293377,1789663354,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},962085662650,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits  \nBohan Li1 , Jiannan Guan1 , Longxu Dou2 , Yunlong Feng1 , Dingzirui Wang1 ,  \nYang Xu1 , Enbo Wang1 , Qiguang Chen1 , Bichen Wang1 , Xiao Xu1 , Yimeng Zhang2 , Libo Qin3 , Yanyan Zhao1 , Qingfu Zhu1 , Wanxiang Che1 *  \n1 Harbin Institute of Technology 2 Individual Researcher 3 Central South University  \n{bhli,[jnguan}@ir.hit.edu.cn](jnguan}@ir.hit.edu.cn), [car@ir.hit.edu.cn](car@ir.hit.edu.cn)  \nAbstract  \nThe Myers-Briggs Type Indicator (MBTI) isone of the most influential personality theories reflecting individual differences in thinking, feeling, and behaving. MBTI personality detection has garnered considerable research interest and has evolved significantly over the years. However, this task tends to be overly optimistic, as it currently does not align well with the natural distribution of population personality traits. Specifically,(1) the self-reported labels in existing datasets result in incorrect labeling issues, and (2) the hard labels fail to capture the full range of population personality distributions. In this paper, we optimize the task by constructing MBTIBENCH, the first manually annotated high-quality MBTI personality detection dataset with soft labels, under the guidance of psychologists. As for the first challenge, MBTIBENCH effectively solves the incorrect labeling issues, which account for 29.58% of the data. As for the second challenge, we estimate soft labels by deriving the polarity tendency of samples. The obtained soft labels confirm that there are more people with non-extreme personality traits. Experimental results not only highlight the polarized predictions and biases in LLMs as key directions for future research, but also confirm that soft labels can provide more benefits to other psychological tasks than hard labels.1  \n1 Introduction  \nPersonality, a key psychological concept, refers to individual differences in thinking, feeling, and behavior (Corr and Matthews, 2009) . Among personality models, the Myers-Briggs Type Indicator (MBTI) is one of the most recognized nonclinical frameworks, with broad applications in areas like stance detection (Hosseinia et al., 2021) . Recently, automatically detecting a person’s MBTI  \n* Corresponding author.  \n1The code and data are available at [https://github.com/](https://github.com/)[ ](https://github.com/)Personality-NLP/MbtiBench.  \n(a) Dataset quality issues in existing datasets.  \n(b) Lack of soft labels in existing datasets.  \nFigure 1: Our MBTIBENCH focuses on the above two limitations in existing MBTI personality detection datasets: data quality issues and the lack of soft labels.  \ntype from their written content (e.g., tweets and blogs) (Plank and Hovy, 2015 ; Gjurkovi et al., 2021) has become a growing area of research with both academic significance (Stajner and Yenikent, 2020 ; Khan et al., 2005) and practical applications (Bagby et al., 2016) .  \nHowever, MBTI personality detection tends to be overly optimistic, as it currently does not align well with the natural distribution of personality traits within the population. (1) Given the widespread use of MBTI, existing datasets are often derived from social media posts, with labels typically sourced from users’ self-reported MBTI types (Gjurkovi et al., 2021) . Assessments conducted by non-professional psychological institutions, along with inaccurate self-perception, can lead to a mismatch between the self-reported personality traits and the actual linguistic patterns in the text (McDonald, 2008 ; Müller and  \n5071  \nProceedings of the 31st International Conference on Computational Linguistics, pages 5071–5081 January 19–24, 2025 . ©2025 Association for Computational Linguistics  \n\n| Attitudes | Perceiving Functions | Judging Functions | Lifestyle Preferences |\n| --- | --- | --- | --- |\n| Interact with the world | Gather information | Make decisions | Organize their lives |","cbCaiptvy1gP36Do","https://ap.wps.com/l/cbCaiptvy1gP36Do","pdf",1319510,11,"English","# Abstract\n# Introduction\n## Dataset limitations\n## Proposed solution: MBTIBENCH","[{\"question\":\"What problem does MBTIBENCH address in existing MBTI personality detection datasets?\",\"answer\":\"It targets two limitations: incorrect labels arising from self-reported MBTI types and the lack of soft labels that fail to reflect the full population trait spectrum.\"},{\"question\":\"How does MBTIBENCH handle label noise from self-reported MBTI types?\",\"answer\":\"It proposes data filtering guidelines for MBTI detection and manually re-annotates the cleaned datasets with labels that best describe each post’s personality polarity.\"},{\"question\":\"What are soft labels in MBTIBENCH and why are they important?\",\"answer\":\"Soft labels are estimated by deriving the polarity tendency of samples, allowing the dataset to represent non-extreme personality traits rather than only binary categories.\"}]","Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits | PDF",28]