{"version":"1.0","provider_name":"DagligaAI","provider_url":"https:\/\/dailyai.com\/sv","author_name":"Eugene van der Watt","author_url":"https:\/\/dailyai.com\/sv\/author\/eugene\/","title":"System 2 Attention improves accuracy of LLM responses | DailyAI","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"PPKvb0W1x5\"><a href=\"https:\/\/dailyai.com\/sv\/2023\/11\/system-2-attention-improves-accuracy-of-llm-responses\/\">System 2 Attention f\u00f6rb\u00e4ttrar noggrannheten i LLM-svaren<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/dailyai.com\/sv\/2023\/11\/system-2-attention-improves-accuracy-of-llm-responses\/embed\/#?secret=PPKvb0W1x5\" width=\"600\" height=\"338\" title=\"&quot;System 2 Attention f\u00f6rb\u00e4ttrar tr\u00e4ffs\u00e4kerheten i LLM-svar&quot; - DailyAI\" data-secret=\"PPKvb0W1x5\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/dailyai.com\/wp-includes\/js\/wp-embed.min.js\n<\/script>","thumbnail_url":"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/11\/Simplify.jpg","thumbnail_width":1000,"thumbnail_height":666,"description":"Large Language Models (LLM) are often mislead by bias or irrelevant context in a prompt. Researchers at Meta have found a seemingly simple way to fix that. As context windows increase the prompts that we enter into an LLM can become longer and increasingly detailed. LLMs have become better at picking up on the nuances or smaller details in our prompts, but sometimes this can confuse them. Early machine learning used a \u201chard attention\u201d approach that singled out the most relevant part of an input and responded only to that. This works fine when you\u2019re trying to caption an image,"}