[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-503096-105":59,"doc-detail-503096-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","evidence-of-interrelated-cognitive-like-capabilities-in-large-language-models-indications-of-artificial-general-intelligence-or-achievement","Evidence of interrelated cognitive-like capabilities in large language models - Indications of artificial general intelligence or achievement?","","Large language models (LLMs) are advanced AI systems capable of performing many tasks found in human intelligence tests, yet individuals differ substantially in their capacities. Building on findings of positive manifold structure and a general intelligence factor in human data, the study hypothesized analogous positive intercorrelations among LLM test results. Using 591 LLMs and 12 tests spanning fluid reasoning, domain knowledge, reading/writing, and quantitative knowledge, the work reports strong evidence for a positive manifold and a general ability factor.",{"@graph":69,"@context":122},[70,84,105],{"@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/evidence-of-interrelated-cognitive-like-capabilities-in-large-language-models-indications-of-artificial-general-intelligence-or-achievement/503096/",{"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/evidence-of-interrelated-cognitive-like-capabilities-in-large-language-models-indications-of-artificial-general-intelligence-or-achievement/503096.png","ImageObject",300,407,{"name":92,"@type":93},"Fahsai","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the study investigate about large language models (LLMs)?","Question",{"text":112,"@type":113},"It examines whether LLM performance shows positive correlations across multiple human-like cognitive abilities and whether an overall general ability factor may emerge.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the evidence tested in the study?",{"text":117,"@type":113},"The study used scores from 12 tests aligned with fluid reasoning, domain-specific knowledge, reading/writing, and quantitative knowledge across 591 LLMs.",{"name":119,"@type":110,"acceptedAnswer":120},"How do model size (number of parameters) relate to cognitive-like ability factors?",{"text":121,"@type":113},"The number of parameters correlated positively with the general ability factor and a combined domain-knowledge/reading-writing factor, with diminishing returns for larger models.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},503096,1791492823,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},549768702563,"https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083","Intelligence 106 (2024) 101858  \nContents lists available at ScienceDirect  \nIntelligence  \njournal [homepage:](homepage: www.elsevier.com/locate/intell)[ www.elsevier.com/locate/intell](homepage: www.elsevier.com/locate/intell)  \n| Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement? |  |  | |\n| --- | --- | --- | --- |\n| David Ili´c a, Gilles E. Gignac b,*\u003Cbr>a Independent Researcher, Belgrade, Serbia\u003Cbr>b School of Psychological Science, University of Western Australia, Australia |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Artificial intelligence\u003Cbr>Artificial general intelligence Number of parameters |  | Large language models (LLMs) are advanced artificial intelligence (AI) systems that can perform a variety of tasks commonly found in human intelligence tests, such as defining words, performing calculations, and engaging in verbal reasoning. There are also substantial individual differences in LLM capacities. Given the consistent observation of a positive manifold and general intelligence factor in human samples, along with group-level factors (e.g., crystallised intelligence), we hypothesized that LLM test scores may also exhibit positive intercorrelations, which could potentially give rise to an artificial general ability (AGA) factor and one or more group-level factors. Based on a sample of 591 LLMs and scores from 12 tests aligned with fluid reasoning (Gf), domain-specific knowledge (Gkn), reading/writing (Grw), and quantitative knowledge (Gq), we found strong empirical evidence for a positive manifold and a general factor of ability. Additionally, we identified a combined Gkn/Grw group-level factor. Finally, the number of LLM parameters correlated positively with both general factor of ability and Gkn/Grw factor scores, although the effects showed diminishing returns. We interpreted our results to suggest that LLMs, like human cognitive abilities, may share a common underlying efficiency in processing information and solving problems, though whether LLMs manifest primarily achievement/expertise rather than intelligence remains to be determined. Finally, while models with greater numbers of parameters exhibit greater general cognitive-like abilities, akin to the connection between greater neuronal density and human general intelligence, other characteristics must also be involved. |  |\n\n1. Introduction  \nGignac and Szodorai (2024, p. 4) defined artificial intelligence (AI) as “an artificial system’s maximal capacity to complete a novel standardized task with veridical scoring using computational algorithms”. Currently, much attention has focussed on the development and testing of large language models (LLMs), sophisticated AI systems that leverage extensive datasets and advanced neural network architectures (Zhao et al., 2023). LLMs, such as GPT, Claude, LLaMA, and Gemini, are capable of performing a wide range of tasks, including defining words, retrieving factual information, summarization, performing calculations, verbal reasoning, and creative writing.  \nA large number of modern LLMs have been developed since the introduction of transformer technology (Zhao et al., 2023). Like humans, LLMs exhibit substantial individual differences in capacities, as evidenced by their varied performance across a diversity of tasks (Owen, 2024). Some LLMs excel in specific domains due to specialized training  \ndata or enhanced architectural features, while others perform more robustly across a broader spectrum of tasks (Dong et al., 2023; Lin et al., 2023). This variability in performance suggests that, similar to human intelligence, there might be one or more underlying dimensions that represent LLM performance. Understanding these individual differences may prove useful for advancing our knowledge of AI capabilities and designing more effective and versatile AI systems. Additionally, scientific discoveries into ","cbCaiseSQbAd6KqR","https://ap.wps.com/l/cbCaiseSQbAd6KqR","pdf",2969006,11,"English","# Introduction\n## General intelligence: human and artificial","[{\"question\":\"What does the study investigate about large language models (LLMs)?\",\"answer\":\"It examines whether LLM performance shows positive correlations across multiple human-like cognitive abilities and whether an overall general ability factor may emerge.\"},{\"question\":\"How was the evidence tested in the study?\",\"answer\":\"The study used scores from 12 tests aligned with fluid reasoning, domain-specific knowledge, reading/writing, and quantitative knowledge across 591 LLMs.\"},{\"question\":\"How do model size (number of parameters) relate to cognitive-like ability factors?\",\"answer\":\"The number of parameters correlated positively with the general ability factor and a combined domain-knowledge/reading-writing factor, with diminishing returns for larger models.\"}]","Evidence of interrelated cognitive-like capabilities in large language models - Indications of artificial general intelligence or achievement? | PDF",1791050141,28]