{"id":55021,"date":"2023-07-22T16:31:29","date_gmt":"2023-07-22T20:31:29","guid":{"rendered":"https:\/\/coinscreed.com\/staging\/?p=55021"},"modified":"2023-07-22T17:34:24","modified_gmt":"2023-07-22T21:34:24","slug":"ai21-labs-debuts-anti-hallucination-feature-for-gpt-chatbots","status":"publish","type":"post","link":"https:\/\/coinscreed.com\/staging\/ai21-labs-debuts-anti-hallucination-feature-for-gpt-chatbots\/","title":{"rendered":"AI21 Labs Debuts Anti-hallucination Feature for GPT Chatbots"},"content":{"rendered":"\n<p><a href=\"https:\/\/coinscreed.com\/staging\/terraform-labs-seeks-access-to-ftx-wallets.html\" target=\"_blank\" rel=\"noreferrer noopener\">AI21 Labs<\/a> has unveiled &#8220;Contextual Answers,&#8221; a question-answering engine for large language models (LLMs). <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-1024x576.jpg\" alt=\"AI21 Labs Debuts Anti-hallucination Feature for GPT Chatbots\" class=\"wp-image-55023\" srcset=\"https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-1024x576.jpg 1024w, https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-300x169.jpg 300w, https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-768x432.jpg 768w, https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-750x422.jpg 750w, https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183-1140x641.jpg 1140w, https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183.jpg 1200w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">AI21 Labs Debuts Anti-hallucination Feature for GPT Chatbots<\/figcaption><\/figure>\n\n\n\n<p>The new engine allows users to add their data libraries when connected to an LLM, limiting the model's outputs to certain data. ChatGPT and comparable <a href=\"https:\/\/coinscreed.com\/staging\/the-role-of-artificial-intelligence-in-fintech.html\" target=\"_blank\" rel=\"noreferrer noopener\">artificial intelligence<\/a> (AI) solutions have fundamentally changed the AI market, yet adoption by many enterprises is challenging due to a lack of confidence.<\/p>\n\n\n\n<p>Employees, according to a study, look for information for over half of their working hours. The possibilities for chatbots that can perform search operations are enormous, but most aren't designed with enterprise in mind.<\/p>\n\n\n\n<p>Created by AI21, Contextual Answers allows customers to pipeline their own data and document libraries, bridging the gap between chatbots made for general usage and enterprise-level question-answering services.<\/p>\n\n\n\n<p>Contextual Answers, according to a blog post from AI21, allow users to direct AI responses without retraining models, reducing some of the adoption's largest barriers:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>&#8220;Most businesses struggle to adopt [AI], citing cost, complexity and lack of the models' specialization in their organizational data, leading to responses that are incorrect, &#8216;hallucinated' or inappropriate for the context.&#8221;<\/p>\n<\/blockquote>\n\n\n\n<p>Teaching LLMs to communicate a lack of confidence is one of the unsolved problems in creating practical LLMs, such as <a href=\"https:\/\/coinscreed.com\/staging\/bybit-integrates-with-chatgpt-to-launch-toolsgpt.html\" target=\"_blank\" rel=\"noreferrer noopener\">OpenAI's ChatGPT<\/a> or Google's Bard.<\/p>\n\n\n\n<p>A chatbot will typically respond to a user's query even if its data set lacks sufficient knowledge to provide factual information. LLMs frequently output false information in these situations instead of a low-confidence response like &#8220;I don't know.&#8221;<\/p>\n\n\n\n<p>Researchers call These results &#8221; hallucinations &#8221; because the machines produce information that appears missing from their data sets, much like people who perceive things that aren't there.<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-rich is-provider-twitter wp-block-embed-twitter\"><div class=\"wp-block-embed__wrapper\">\n<div class=\"embed-twitter\"><blockquote class=\"twitter-tweet\" data-width=\"550\" data-dnt=\"true\"><p lang=\"en\" dir=\"ltr\">We&#39;re excited to introduce Contextual Answers, an API solution where answers are based on organizational knowledge, leaving no room for AI hallucinations. \ud83d\udcad<br><br>\u27a1\ufe0f <a href=\"https:\/\/t.co\/LqlyBz6TYZ\" target=\"_blank\">https:\/\/t.co\/LqlyBz6TYZ<span class=\"wpil-link-icon\" title=\"Link goes to external site.\" style=\"margin: 0 0 0 5px;\"><svg width=\"24\" height=\"24\" style=\"height:16px; width:16px; fill:#000000; stroke:#000000; display:inline-block;\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" xmlns:svg=\"http:\/\/www.w3.org\/2000\/svg\"><g id=\"wpil-svg-outbound-7-icon-path\" fill=\"none\" clip-path=\"url(#clip0_31_188)\">\r\n                            <path d=\"M9.16724 14.8891L20.1672 3.88908\" stroke-linecap=\"round\"\/>\r\n                            <path d=\"M13.4497 3.53554L20.5208 3.53554L20.5208 10.6066\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\r\n                            <path d=\"M17.5 13.5L17.5 16.26C17.5 17.4179 17.5 17.9968 17.2675 18.4359C17.0799 18.7902 16.7902 19.0799 16.4359 19.2675C15.9968 19.5 15.4179 19.5 14.26 19.5L7.74 19.5C6.58213 19.5 6.0032 19.5 5.56414 19.2675C5.20983 19.0799 4.92007 18.7902 4.73247 18.4359C4.5 17.9968 4.5 17.4179 4.5 16.26L4.5 9.74C4.5 8.58213 4.5 8.0032 4.73247 7.56414C4.92007 7.20983 5.20982 6.92007 5.56414 6.73247C6.0032 6.5 6.58213 6.5 7.74 6.5L11 6.5\" stroke-linecap=\"round\"\/>\r\n                        <\/g>\r\n                        <defs>\r\n                            <clipPath id=\"clip0_31_188\">\r\n                                <rect fill=\"white\" height=\"24\" width=\"24\"\/>\r\n                            <\/clipPath>\r\n                        <\/defs><\/svg><\/span><\/a> <a href=\"https:\/\/t.co\/uBrXrngXhW\" target=\"_blank\">pic.twitter.com\/uBrXrngXhW<span class=\"wpil-link-icon\" title=\"Link goes to external site.\" style=\"margin: 0 0 0 5px;\"><svg width=\"24\" height=\"24\" style=\"height:16px; width:16px; fill:#000000; stroke:#000000; display:inline-block;\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" xmlns:svg=\"http:\/\/www.w3.org\/2000\/svg\"><use href=\"#wpil-svg-outbound-7-icon-path\"><\/use><\/svg><\/span><\/a><\/p>&mdash; AI21 Labs (@AI21Labs) <a href=\"https:\/\/twitter.com\/AI21Labs\/status\/1681659969517568001?ref_src=twsrc%5Etfw\" target=\"_blank\" rel=\"noopener\">July 19, 2023<span class=\"wpil-link-icon\" title=\"Link goes to external site.\" style=\"margin: 0 0 0 5px;\"><svg width=\"24\" height=\"24\" style=\"height:16px; width:16px; fill:#000000; stroke:#000000; display:inline-block;\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" xmlns:svg=\"http:\/\/www.w3.org\/2000\/svg\"><use href=\"#wpil-svg-outbound-7-icon-path\"><\/use><\/svg><\/span><\/a><\/blockquote><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/div>\n<\/div><\/figure>\n\n\n\n<p>A121 states that Contextual Answers should eliminate the hallucination issue by only displaying information when it is pertinent to user-provided documentation or by not displaying any information.<\/p>\n\n\n\n<p>The introduction of<a href=\"https:\/\/www.bing.com\/search?q=AI21+Labs+Debuts+Anti-hallucination+Feature+for+GPT+Chatbots&qs=n&form=QBRE&sp=-1&lq=0&pq=&sc=10-0&sk=&cvid=CFFBC68BF1FC4EC897793C3062386AF9&ghsh=0&ghacc=0&ghpl=\" target=\"_blank\" rel=\"noreferrer noopener\"> generative pretrained transformer<span class=\"wpil-link-icon\" title=\"Link goes to external site.\" style=\"margin: 0 0 0 5px;\"><svg width=\"24\" height=\"24\" style=\"height:16px; width:16px; fill:#000000; stroke:#000000; display:inline-block;\" viewBox=\"0 0 24 24\" version=\"1.1\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" xmlns:svg=\"http:\/\/www.w3.org\/2000\/svg\"><use href=\"#wpil-svg-outbound-7-icon-path\"><\/use><\/svg><\/span><\/a> (GPT) systems has had various effects in industries where precision is more crucial than automation, such as finance and law.<\/p>\n\n\n\n<p>Due to the tendency of GPT systems to confuse or hallucinate information, even when connected to the internet and capable of referring to sources, experts continue to advise care when employing them in the finance industry. <\/p>\n\n\n\n<p>In the legal field, a lawyer who relied on ChatGPT outputs during a lawsuit is now subject to penalties and sanctions. AI21 has shown mitigation for the hallucination issue by front-loading AI systems with useful data and acting before the system can hallucinate false information.<\/p>\n\n\n\n<p>This could lead to widespread adoption, especially in the fintech sector, where traditional financial institutions have been hesitant to use GPT technology and the cryptocurrency and <a href=\"https:\/\/coinscreed.com\/staging\/nft-connect-musical-communities-to-blockchain-ecosystems.html\" target=\"_blank\" rel=\"noreferrer noopener\">blockchain communities<\/a> have had mixed success using chatbots.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI21 Labs has unveiled &#8220;Contextual Answers,&#8221; a question-answering engine for large language models (LLMs). The new engine allows users to add their data libraries when connected to an LLM, limiting the model&#8217;s outputs to certain data. ChatGPT and comparable artificial intelligence (AI) solutions have fundamentally changed the AI market, yet adoption by many enterprises is [&hellip;]<\/p>\n","protected":false},"author":43,"featured_media":55023,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[21],"tags":[15417,15419,15418],"class_list":["post-55021","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","tag-ai21","tag-chatbots","tag-gpt"],"jetpack_featured_media_url":"https:\/\/coinscreed.com\/staging\/wp-content\/uploads\/2023\/07\/croc_1690055615183.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/posts\/55021","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/users\/43"}],"replies":[{"embeddable":true,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/comments?post=55021"}],"version-history":[{"count":0,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/posts\/55021\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/media\/55023"}],"wp:attachment":[{"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/media?parent=55021"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/categories?post=55021"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/coinscreed.com\/staging\/wp-json\/wp\/v2\/tags?post=55021"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}