[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85863-en":3,"doc-seo-85863-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85863,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","PTEI Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models","Large Language Models continue to lag humans in complex emotional reasoning because individual differences, especially personality traits, are not sufficiently integrated into Emotional Intelligence (EI) pipelines. PTEI proposes extracting MBTI and OCEAN personality traits from given emotional scenarios, then using them as contextual knowledge in personality-aware LLM prompts to infer emotions and underlying causes. A contrastive-learning retrieval system selects emotionally and personally aligned scenarios to improve grounding. Experiments on EI benchmarks show improved Emotional Understanding, with GPT gains reaching an additional 4% when combined with Chain-of-Thought.","PTEI: Integrating Personality Traits to Enhance Emotional Intelligence in Large Language Models  \nAmir Reza Jafari  \nSamovar, Telecom SudParis, Institut Polytechnique de Paris  \nPalaiseau, France  \nPraboda Rajapaksha† Department of Computer Science, Aberystwyth University Aberystwyth, Wales  \narXiv :2607 . 10245v 1 [ cs .CL] 11 Jul 2026  \nReza Farahbakhsh  \nSamovar, Telecom SudParis, Institut Polytechnique de Paris  \nPalaiseau, France  \nAbstract  \nDespite advances in Emotional Intelligence (EI), Large Language Models (LLMs) still significantly underperform humans in complex emotional reasoning. This gap originates partly from the limited incorporation of individual differences, particularly personality traits, which are fundamental to human emotional inference. To address this, we propose PTEI, a novel framework for integrating Personality Traits into Emotional Intelligence tasks using LLMs. In PTEI, MBTI and OCEAN personality traits are first extracted directly from the given emotional scenarios and then utilized as contextual knowledge within personality-aware prompts, guiding LLMs to accurately infer emotions and their underlying causes. To ensure optimal contextual grounding, we employ Contrastive Learning to construct an optimized retrieval system that surfaces emotionally and personally aligned scenarios, enhancing reasoning quality. Extensive experiments on established EI benchmarks show that PTEI enhances Emotional Understanding (EU) capabilities of various LLMs in EI, with the strongest improvement observed in GPT models, where combining PTEI with Chain-of-Thought (CoT) reasoning yields an additional 4% increase in accuracy. These findings underscore PTEI’s contribution toward advancing AI systems with more sophisticated social and psychological grounding.  \nKeywords  \nEmotional Intelligence, Emotion Detection and Analysis, Language Modeling, Personality Traits, Social Science, Large Language Models  \n1 Introduction  \nEmotional intelligence (EI), the ability to perceive, understand, regulate, and express emotions, is essential for effective communication, social interaction, and decision making [6, 8, 24] . As Large Language Models (LLMs) are increasingly deployed in human-facing  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission [and/or a fee. Request permissions from permissions@acm.org](and/or a fee. Request permissions from permissions@acm.org).  \nConference’17, Washington, DC, USA  \n© 2026 Copyright held by the owner/author(s) . Publication rights licensed to ACM. ACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM [https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \nNoel Crespi  \nSamovar, Telecom SudParis, Institut Polytechnique de Paris  \nPalaiseau, France  \n􀃏  \nAn Emotional Underestanding Scenario  \nJacob has a hard time dealing with people and is very antisocial. He can easily get overwhelmed by talking to others. Tonight, after work, he spent a lonely night all by himself watching movies.  \n(b) (c)  \n􀁂  \n(d)  \n􀂉􀁂  \n(e)  \nFigure 1: Illustration of PTEI’s impact on emotional inference in LLMs. (a) An emotionally ambiguous scenario featuring Jacob. (b) The LLM’s task: a multiple-choice question asks for both Jacob’s emotion and its underlying cause. (c) Personality trait information extracted for Jacob. (d) The baseline LLM without personality knowledge misinterprets Jacob’s solitude as sadness. (e) Our PTEI framework correctly infersthe emotion as joy, recognizing that Jacob’s introverted and self-reliant personality makes soli","cbCaisQaSG9k7h9V","https://ap.wps.com/l/cbCaisQaSG9k7h9V","pdf",1734533,1,15,"English","en",105,"# Introduction\n## Emotional Intelligence in LLMs\n## EI benchmarks and limitations\n## Personality traits and emotional inference\n# Proposed PTEI Framework\n## Personality trait extraction and contextual prompting\n## Contrastive learning retrieval for grounding\n# Experiments and Results\n## Evaluation on EI benchmarks\n## Impact of Chain-of-Thought with PTEI\n# Conclusion\n## Social and psychological grounding for AI","[{\"question\":\"Why do large language models underperform humans in complex emotional reasoning?\",\"answer\":\"Because current EI approaches do not incorporate individual differences, particularly personality traits, which are essential for human emotional inference.\"},{\"question\":\"How does PTEI integrate personality traits into emotional intelligence tasks?\",\"answer\":\"PTEI extracts MBTI and OCEAN traits from emotional scenarios, then injects the traits as contextual knowledge in personality-aware prompts to guide emotion and cause inference.\"},{\"question\":\"What role does contrastive learning play in PTEI?\",\"answer\":\"It builds an optimized retrieval system that surfaces scenarios aligned with both the emotion and the individual’s personality, improving the model’s contextual grounding and reasoning quality.\"}]",1784206774,38,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"ptei-integrating-personality-traits-to-enhance-emotional-intelligence-in-large-language-models","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/ptei-integrating-personality-traits-to-enhance-emotional-intelligence-in-large-language-models/85863/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do large language models underperform humans in complex emotional reasoning?","Question",{"text":75,"@type":76},"Because current EI approaches do not incorporate individual differences, particularly personality traits, which are essential for human emotional inference.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does PTEI integrate personality traits into emotional intelligence tasks?",{"text":80,"@type":76},"PTEI extracts MBTI and OCEAN traits from emotional scenarios, then injects the traits as contextual knowledge in personality-aware prompts to guide emotion and cause inference.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does contrastive learning play in PTEI?",{"text":84,"@type":76},"It builds an optimized retrieval system that surfaces scenarios aligned with both the emotion and the individual’s personality, improving the model’s contextual grounding and reasoning 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