{"id":2956,"date":"2022-10-07T00:06:43","date_gmt":"2022-10-07T00:06:43","guid":{"rendered":"https:\/\/autogenai.dsstaging2.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/"},"modified":"2025-03-20T20:53:47","modified_gmt":"2025-03-20T20:53:47","slug":"training-language-engines-to-promote-inclusion","status":"publish","type":"post","link":"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/","title":{"rendered":"Training Language Engines to Promote AI &amp; Inclusion"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#Training_Large_Language_Models\" >Training Large Language Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#Prompt_Engineering\" >Prompt Engineering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#How_We_Use_Prompt_Engineering\" >How We Use Prompt Engineering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#Training_Large_Language_Models-2\" >Training Large Language Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#Prompt_Engineering-2\" >Prompt Engineering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/autogenai.com\/apac\/blog\/training-language-engines-to-promote-inclusion\/#How_We_Use_Prompt_Engineering-2\" >How We Use Prompt Engineering<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Training_Large_Language_Models\"><\/span><b>Training Large Language Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Large language models power language Engines. These trained models produce the highest probability of the next word based on the preceding words. For example, given the words \u201cThe cat sat on the\u201d, the highest probability next word is \u201cmat\u201d.<\/p>\n<h3><b>Assumptions of Highest Probability Next Word<\/b><\/h3>\n<p>Sometimes the highest probability next word will be morally undesirable. This can occur when the new word derives from biassed, offensive or gendered assumptions. For example, assuming that a professor is male.<\/p>\n<h3><b>Configuring Against Biases<\/b><\/h3>\n<p>Language Engines are configurable to try and avoid these biases using a number of techniques. At AutogenAI we are working to build Language Engines that reflect the modern world as we would want it to be. Therefore, they aim to be diverse, inclusive and welcoming. Below are some of the technical ways that we and other machine learning engineers are doing this:<\/p>\n<h3><b>Inclusive data<\/b><\/h3>\n<p>We train Large Language Models (LLMs) using only a subset of all available text data. <a href=\"https:\/\/arxiv.org\/abs\/2104.08758\">Outlined in this paper<\/a> is the job of cleaning up the \u201cCommon Crawl corpus\u201d into \u201cThe Colossal Clean Crawled Corpus\u201d. Removing &amp; cleaning offensive and inaccurate content before training the models goes some way to eliminating bias.<\/p>\n<h3><b>What is The Common Crawl Corpus?<\/b><\/h3>\n<p>The Common Crawl Corpus is a vast webarchive consisting of petabytes of data collected since 2011.<\/p>\n<h3><b>Human evaluation and re-training<\/b><\/h3>\n<p>Humans are increasingly manually evaluating the output produced by LLMs for accuracy and inclusivity. This human feedback feeds into the models, training them to make fewer factual errors. Therefore, avoiding offensive and toxic language, and producing more relevant and diverse responses.<\/p>\n<p>OpenAI trained their large language model using the following three steps:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/4BjvrstzSFGqlfVS5UEz.webp\" alt=\"\" width=\"1024\" height=\"625\" \/><\/p>\n<p><strong>Evaluation and retraining of an LLM<\/strong><br \/>\nA diagram illustrating the three steps to evaluate and retrain a LLM: (1) supervised fine-tuning, (2) reward model training, and (3) reinforcement learning via proximal policy optimization.<br \/>\nCredit: OpenAI[\/caption]<\/p>\n<h3><b>Word bias neutralisation<\/b><\/h3>\n<p>Machine learning engineers neutralise well-known biases from certain words. For example, removing gender biases from words like \u201cbabysitter\u201d and \u201cdoctor.\u201d Eensuring that they are equally likely to be part of a sentence describing a female or male occupation.<\/p>\n<h3><b>Intentional Gender Distinctions<\/b><\/h3>\n<p>Achieved through a process that takes every word and adjusts the relative positioning between them. This is to ensure that only words that have intentionally gendered distinctions lie along that vector direction. Gendered distinct words include terms such as \u201cking\u201d and \u201cqueen\u201d or \u201che\u201d and \u201cshe\u201d.<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/zPwcPUHyTqqvcOLSK2wG.webp\" alt=\"\" width=\"1024\" height=\"413\" \/><figcaption class=\"wp-caption-text\"><strong>Word vector bias nautralisation<\/strong><br \/>An example of how the words \u201cDoctor\u201d and \u201cBabysitter\u201d can be gender neutralized by using known genderd word vectors.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Prompt_Engineering\"><\/span><b>Prompt Engineering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Altering the text used to interact with large language models has a huge impact on the output. This is through a technique known as \u201cpromote Engineering\u201d.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_We_Use_Prompt_Engineering\"><\/span><b>How We Use Prompt Engineering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>By continuously testing how the model responds to different inputs, AutogenAI\u2019s Prompt Engineers remove a significant amount of output bias. Using the same technique, we can also incorporate our clients\u2019 corporate language. Therefore, win themes, values and priorities to produce company-specific outputs.<\/p>\n<h3><b>Conclusion of AI &amp; Inclusion<\/b><\/h3>\n<p>Language Engines are rapidly becoming key co-producers of the written content that we consume. It is vital that those of us building Language Engines work to ensure that the text produced is inclusive. Reflecting the diversity of the society that we will live in.<\/p>\n<h3><b>AutogenAI\u2019s Work<\/b><\/h3>\n<p>AutogenAI\u2019s team of fine tuners, prompt engineers, developers and writing specialists work with our clients to responsibly use language engines. Filtering out plagiarism, bias and inaccuracy, while embedding company beliefs and values into all content produced. Find out more on our <a href=\"https:\/\/autogenai.com\/use-cases\/\">Case Studies page<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Training_Large_Language_Models-2\"><\/span><b>Training Large Language Models<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Large language models power language Engines. These trained models produce the highest probability of the next word based on the preceding words. For example, given the words \u201cThe cat sat on the\u201d, the highest probability next word is \u201cmat\u201d.<\/p>\n<h3><b>Assumptions of Highest Probability Next Word<\/b><\/h3>\n<p>Sometimes the highest probability next word will be morally undesirable. This can occur when the new word derives from biassed, offensive or gendered assumptions. For example, assuming that a professor is male.<\/p>\n<h3><b>Configuring Against Biases<\/b><\/h3>\n<p>Language Engines are configurable to try and avoid these biases using a number of techniques. At AutogenAI we are working to build Language Engines that reflect the modern world as we would want it to be. Therefore, they aim to be diverse, inclusive and welcoming. Below are some of the technical ways that we and other machine learning engineers are doing this:<\/p>\n<h3><b>Inclusive data<\/b><\/h3>\n<p>We train Large Language Models (LLMs) using only a subset of all available text data. <a href=\"https:\/\/arxiv.org\/abs\/2104.08758\">Outlined in this paper<\/a> is the job of cleaning up the \u201cCommon Crawl corpus\u201d into \u201cThe Colossal Clean Crawled Corpus\u201d. Removing &amp; cleaning offensive and inaccurate content before training the models goes some way to eliminating bias.<\/p>\n<h3><b>What is The Common Crawl Corpus?<\/b><\/h3>\n<p>The Common Crawl Corpus is a vast webarchive consisting of petabytes of data collected since 2011.<\/p>\n<h3><b>Human evaluation and re-training<\/b><\/h3>\n<p>Humans are increasingly manually evaluating the output produced by LLMs for accuracy and inclusivity. This human feedback feeds into the models, training them to make fewer factual errors. Therefore, avoiding offensive and toxic language, and producing more relevant and diverse responses.<\/p>\n<p>OpenAI trained their large language model using the following three steps:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/4BjvrstzSFGqlfVS5UEz.webp\" alt=\"\" width=\"1024\" height=\"625\" \/><\/p>\n<p><strong>Evaluation and retraining of an LLM<\/strong><br \/>\nA diagram illustrating the three steps to evaluate and retrain a LLM: (1) supervised fine-tuning, (2) reward model training, and (3) reinforcement learning via proximal policy optimization.<br \/>\nCredit: OpenAI[\/caption]<\/p>\n<h3><b>Word bias neutralisation<\/b><\/h3>\n<p>Machine learning engineers neutralise well-known biases from certain words. For example, removing gender biases from words like \u201cbabysitter\u201d and \u201cdoctor.\u201d Eensuring that they are equally likely to be part of a sentence describing a female or male occupation.<\/p>\n<h3><b>Intentional Gender Distinctions<\/b><\/h3>\n<p>Achieved through a process that takes every word and adjusts the relative positioning between them. This is to ensure that only words that have intentionally gendered distinctions lie along that vector direction. Gendered distinct words include terms such as \u201cking\u201d and \u201cqueen\u201d or \u201che\u201d and \u201cshe\u201d.<\/p>\n<figure style=\"width: 1024px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/autogenai.com\/apac\/wp-content\/uploads\/sites\/5\/2025\/02\/zPwcPUHyTqqvcOLSK2wG.webp\" alt=\"\" width=\"1024\" height=\"413\" \/><figcaption class=\"wp-caption-text\"><strong>Word vector bias nautralisation<\/strong><br \/>An example of how the words \u201cDoctor\u201d and \u201cBabysitter\u201d can be gender neutralized by using known genderd word vectors.<\/figcaption><\/figure>\n<h2><span class=\"ez-toc-section\" id=\"Prompt_Engineering-2\"><\/span><b>Prompt Engineering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Altering the text used to interact with large language models has a huge impact on the output. This is through a technique known as \u201cpromote Engineering\u201d.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_We_Use_Prompt_Engineering-2\"><\/span><b>How We Use Prompt Engineering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>By continuously testing how the model responds to different inputs, AutogenAI\u2019s Prompt Engineers remove a significant amount of output bias. Using the same technique, we can also incorporate our clients\u2019 corporate language. Therefore, win themes, values and priorities to produce company-specific outputs.<\/p>\n<h3><b>Conclusion of AI &amp; Inclusion<\/b><\/h3>\n<p>Language Engines are rapidly becoming key co-producers of the written content that we consume. It is vital that those of us building Language Engines work to ensure that the text produced is inclusive. Reflecting the diversity of the society that we will live in.<\/p>\n<h3><b>AutogenAI\u2019s Work<\/b><\/h3>\n<p>AutogenAI\u2019s team of fine tuners, prompt engineers, developers and writing specialists work with our clients to responsibly use language engines. Filtering out plagiarism, bias and inaccuracy, while embedding company beliefs and values into all content produced. Find out more on our <a href=\"https:\/\/autogenai.com\/use-cases\/\">Case Studies page<\/a>.<\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Training Large Language Models Large language models power language Engines. These trained models produce the highest probability of the next word based on the preceding words. For example, given the words \u201cThe cat sat on the\u201d, the highest probability next word is \u201cmat\u201d. Assumptions of Highest Probability Next Word Sometimes the highest probability next word&#8230;<\/p>\n","protected":false},"author":150,"featured_media":2960,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[],"class_list":["post-2956","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-category-2"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Training Language Engines to Promote AI &amp; Inclusion | AutogenAI APAC<\/title>\n<meta name=\"description\" content=\"Language Engines are transforming the way that we write. 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