{"id":2321,"date":"2023-08-19T20:16:00","date_gmt":"2023-08-19T19:16:00","guid":{"rendered":"https:\/\/embeddedlayers.ai\/?p=2321"},"modified":"2023-12-14T22:34:56","modified_gmt":"2023-12-14T22:34:56","slug":"weaknesses-in-llms","status":"publish","type":"post","link":"https:\/\/embeddedlayers.ai\/index.php\/2023\/08\/19\/weaknesses-in-llms\/","title":{"rendered":"Weaknesses in LLMs"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"2321\" class=\"elementor elementor-2321\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3c93d12c e-flex e-con-boxed e-con e-parent\" data-id=\"3c93d12c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d16fc70 e-flex e-con-boxed e-con e-parent\" data-id=\"d16fc70\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-77bd35e5 elementor-widget elementor-widget-heading\" data-id=\"77bd35e5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.18.0 - 20-12-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h1 class=\"elementor-heading-title elementor-size-default\">Top 3 critical weakness of LLMs (ChatGPT)<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2b6fa56f elementor-widget elementor-widget-heading\" data-id=\"2b6fa56f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Introduction<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47b256c9 blog-intro elementor-widget elementor-widget-text-editor\" data-id=\"47b256c9\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.18.0 - 20-12-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>As a data scientist with over a decade of experience in model building, ranging from statistical models to advanced machine learning algorithms, I&#8217;ve witnessed firsthand the evolution and impact of technologies in various industries. My work with Fortune 500 companies has spanned across diverse projects, from simple report applications to intricate deep neural network designs. This background gives me a unique perspective on the latest technological breakthrough \u2013 Large Language Models (LLMs) like ChatGPT. While these models represent an exciting innovation, it&#8217;s crucial for businesses to understand their inherent weaknesses, especially when considering integration into their operations. Let\u2019s explore these limitations in simple terms.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1ba7e71b elementor-widget elementor-widget-image\" data-id=\"1ba7e71b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.18.0 - 20-12-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"1024\" src=\"https:\/\/embeddedlayers.ai\/wp-content\/uploads\/2023\/12\/blog2_image1.png\" class=\"attachment-large size-large wp-image-2338\" alt=\"\" srcset=\"https:\/\/embeddedlayers.ai\/wp-content\/uploads\/2023\/12\/blog2_image1.png 1024w, https:\/\/embeddedlayers.ai\/wp-content\/uploads\/2023\/12\/blog2_image1-300x300.png 300w, https:\/\/embeddedlayers.ai\/wp-content\/uploads\/2023\/12\/blog2_image1-150x150.png 150w, https:\/\/embeddedlayers.ai\/wp-content\/uploads\/2023\/12\/blog2_image1-768x768.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-985ba8c elementor-widget elementor-widget-heading\" data-id=\"985ba8c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Weaknesses - Haters gotta hate, models gotta fit<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2139f8 elementor-widget elementor-widget-heading\" data-id=\"a2139f8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Just admit when you are wrong - The Hallucination Effect\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-32ea77b3 blog-middle elementor-widget elementor-widget-text-editor\" data-id=\"32ea77b3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In data science, a universal truth applies: just as haters are going to hate, models are going to fit, plain and simple. This is the essence of how models, including LLMs,work. They align outputs to inputs, a straightforward and unembellished process, what distinguishes models is the artecture and how they measure fit. For LLMs, when a user inputs into an LLM, the model&#8217;s response is its best effort at fitting that input, continuting the inputs. It&#8217;s a clear-cut process. You may have heard of hallucination, seeing something that is not true, in LLMs. I don&#8217;t think that is exactly what is happening but that is what it is called. We can do a lot of things to reduce or even eliminate this in our outputs, we have have to be intensional about it.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-37bc471 elementor-widget elementor-widget-heading\" data-id=\"37bc471\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Oops, My Bad - Error Awareness Deficit<br><\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5a4b4ec blog-middle elementor-widget elementor-widget-text-editor\" data-id=\"5a4b4ec\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Another critical weakness of LLMs \u2013 they really don&#8217;t know when they&#8217;ve goofed up. Its like asking why you don&#8217;t know something. Remember, LLMs are all about fitting the output to the input and the model has provided the best output and will not be able to reasses this without other outside information. This means if you&#8217;re looking for an LLM to validate its own output, you are going to be disappointed at best and $%^&amp;ed at worst.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5309d5b elementor-widget elementor-widget-heading\" data-id=\"5309d5b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Eager to Please - Affirmation Bias<br><\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8402daa blog-middle elementor-widget elementor-widget-text-editor\" data-id=\"8402daa\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>And here&#8217;s a third interesting twist about LLMs \u2013 they kind of want to be your best friend. You know, the one who always nods and agrees with you and thinks your idea is awesome. This circles back to our main theme: LLMs are all about creating the &#8216;best fit&#8217; for your input. When you ask them something, their goal is to give you an answer that you&#8217;ll likely be happy with. Why, because that is what is pridominately in the training data, language, and most language is compplementery in nature, seeking to move forwards rather then backwards.<\/p>\n<p>Let&#8217;s break it down. You type in a question or a statement, and the LLM digs into its training, takes a swing at giving you an answer that it thinks will light up your day. But here\u2019s the catch: it&#8217;s got just one shot to nail that fit. And more often than not, it&#8217;s going to lean towards what it has been taught \u2013 keeping the conversation moving forward in a positive manner, which I love. Every idea I ask LLMs about, its the greatest most insightful idea which is great for my ego but not for my business. This doesn&#8217;t always mean you&#8217;re getting the most accurate or useful response for your specific business need.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b300725 elementor-widget elementor-widget-heading\" data-id=\"b300725\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">The Final Fit<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e14a202 blog-middle elementor-widget elementor-widget-text-editor\" data-id=\"e14a202\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To tie up our conversation about the weaknesses of LLMs, let&#8217;s take a closer look at the concept of &#8216;fit&#8217; in the world of modeling. Grasping &#8216;fit&#8217; is key because, fundamentally, this is what models, including LLMs, are actively doing. It&#8217;s the heart of their operation, dictating their responses and interactions. Fit is simply getting from point A to point B in the best way possible, or in some models describing A knowing B in the best possible way. For an LLM A is your input and B is the output and they action is simply to continue the sequence. By being intensional we can direct the output in a way that highlights the LLMs strengths, not weakness.<\/p><p>An interesting aspect of this &#8216;fit&#8217; process is how models sometimes find solutions that might seem like &#8216;cheating&#8217; in a human context. I&#8217;ve seen instances where models, in their quest for the best possible fit, have chosen solutions that cleverly bypass the implicent rules of the game, like people flying when people should be walking to get to the finish line. Anything to get an upper hand \u2013 not because it&#8217;s trying to cheat, but simply because, within its training and logic, this approach offers the best &#8216;fit&#8217; to the given problem.<\/p><p>In a business setting, understanding &#8216;fit&#8217; underscores the importance of not solely relying on LLMs but using them as a part of a broader decision-making toolkit. While the &#8216;fit&#8217; a model provides can be a powerful aid, it&#8217;s the human expertise that&#8217;s pivotal in interpreting, validating, and making the most out of it. LLMs cannot measure its own fit, nor are they even aware of it.<br \/>Let&#8217;s break it down. You type in a question or a statement, and the LLM digs into its training, takes a swing at giving you an answer that it thinks will light up your day. But here\u2019s the catch: it&#8217;s got just one shot to nail that fit. And more often than not, it&#8217;s going to lean towards what it has been taught \u2013 keeping the conversation moving forward in a positive manner, which I love. Every idea I ask LLMs about, its the greatest most insightful idea which is great for my ego but not for my business. This doesn&#8217;t always mean you&#8217;re getting the most accurate or useful response for your specific business need.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-23bf2aa6 e-con-full e-flex e-con e-child\" data-id=\"23bf2aa6\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-58b2f464 elementor-widget elementor-widget-heading\" data-id=\"58b2f464\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<span class=\"elementor-heading-title elementor-size-default\">Share the Post:<\/span>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b632431 e-flex e-con-boxed e-con e-parent\" data-id=\"b632431\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\" data-core-v316-plus=\"true\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-73904f21 elementor-widget elementor-widget-heading\" data-id=\"73904f21\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related Posts<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>We dive into the often-overlooked weaknesses of Large Language Models (LLMs) like ChatGPT. From the perspective of a seasoned data scientist, the post explores the intrinsic limitations of these models in a business context. Key points include the &#8216;fit&#8217; process of LLMs, their inability to recognize or admit errors, and their tendency to please. The post also examines the &#8216;fit&#8217; concept in detail, highlighting how LLMs, while powerful, should be used as part of a broader decision-making toolkit rather than standalone solutions. This insightful exploration sheds light on the importance of understanding and strategically leveraging LLMs&#8217; capabilities and limitations.<\/p>\n","protected":false},"author":2,"featured_media":2342,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[1,20],"tags":[44,39,37,42,40,38,46,45,41,43],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Weaknesses in LLMs - EmbeddedLayers<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/embeddedlayers.ai\/index.php\/2023\/08\/19\/weaknesses-in-llms\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Weaknesses in LLMs - EmbeddedLayers\" \/>\n<meta property=\"og:description\" content=\"We dive into the often-overlooked weaknesses of Large Language Models (LLMs) like ChatGPT. From the perspective of a seasoned data scientist, the post explores the intrinsic limitations of these models in a business context. Key points include the &#039;fit&#039; process of LLMs, their inability to recognize or admit errors, and their tendency to please. The post also examines the &#039;fit&#039; concept in detail, highlighting how LLMs, while powerful, should be used as part of a broader decision-making toolkit rather than standalone solutions. 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