From ad3279b1dbf791dbee0ce2169a60aee282d93726 Mon Sep 17 00:00:00 2001
From: Sarah Oberbichler <66369271+soberbichler@users.noreply.github.com>
Date: Mon, 2 Dec 2024 03:47:10 +0100
Subject: [PATCH] Update module_5.html

---
 modules/module_5.html | 29 ++++++++++++++---------------
 1 file changed, 14 insertions(+), 15 deletions(-)

diff --git a/modules/module_5.html b/modules/module_5.html
index acbe8b4..ccc8eaf 100644
--- a/modules/module_5.html
+++ b/modules/module_5.html
@@ -46,27 +46,26 @@
     <a href="../index.html" class="btn btn-secondary back-button">&larr; Go Back</a>
 
     <div class="container">
-        <h1>Module 4: Large Language Models for Article Extraction and Post-OCR Correction</h1>
+        <h1>Module 5: Large Language Models for Article Extraction and Post-OCR Correction</h1>
         
-        <p>Module 3 will be all about Large Language models, prompting techniques and two specific NLP taks: article extraction and OCR post-correction</p>
+        <p>Module 5 will be all about Large Language models, prompting techniques and two specific NLP taks: article extraction and OCR post-correction</p>
         <ul>
            Large Language Models (LLMs) are artificial intelligence systems trained on massive text datasets that can process and generate human language based on the Transformer architecture introduced by Vaswear et al. in 2017. These models use neural networks to predict likely next tokens in a sequence, enabling tasks like text completion, translation, and question answering. While research shows correlations between model size, training data, and performance, specific capabilities and limitations continue to be actively studied and debated in the research community. They fundamentally operate through pattern matching rather than genuine understanding.
         </ul>
         
-        <h3>Preparation for Module 5:</h3>
-       <ol>
-            <li>
-                <p>Read the article listed under literature below and prepare for class discussion:</p>
-                <ul>
-                    <li>Why are machine learning methods called "Black Boxes"?</li>
-                    <li>What does XAI stand for?</li>
-                    <li>What is a self-attention mechanism?</li>
-                    <li>Name a few methods to look into the "Black Box"</li>
-                    <li>Create at least one more entry in the Glossary</li>
-                </ul>
-            </li>
+      <h3>Preparation for Module 5:</h3>
+<ol>
+    <li>Watch (if not done already) this YouTube Video on LLMs: <a href="https://www.youtube.com/watch?v=LPZh9BOjkQs&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=5">3Blue1Brown: Large Language Models</a></li>
+    <li>Inform yourself: What is Prompt Engineering and what kind of prompting techniques can you find?</li>
+    <li>Create an NVIDIA token:
+        <ol>
+            <li>Visit the <a href="https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct">NVIDIA AI Playground</a></li>
+            <li>Click on login</li>
+            <li>Enter your University Email</li>
+            <li>Copy the token</li>
         </ol>
-
+    </li>
+</ol>
         <h3>Literature:</h3> 
                     <p class="citation">
                         Dobson, J.E. On reading and interpreting black box deep neural networks. Int J Digit Humanities 5, 431–449 (2023). <a href="https://doi.org/10.1007/s42803-023-00075-w" target="_blank">https://doi.org/10.1007/s42803-023-00075-w</a>