From 2edbb22bf97761f7ca7118c75609d2224e9626b6 Mon Sep 17 00:00:00 2001
From: Sarah Oberbichler <66369271+soberbichler@users.noreply.github.com>
Date: Mon, 2 Dec 2024 02:41:59 +0100
Subject: [PATCH] Update module_5.html

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 modules/module_5.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/modules/module_5.html b/modules/module_5.html
index 16a3491..acbe8b4 100644
--- a/modules/module_5.html
+++ b/modules/module_5.html
@@ -50,7 +50,7 @@ <h1>Module 4: Large Language Models for Article Extraction and Post-OCR Correcti
         
         <p>Module 3 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 statistical patterns they've learned. Based on the Transformer architecture introduced by Vaswear et al. in 2017, these models have demonstrated measurable success in tasks like text completion, translation, and answering questions by predicting likely next tokens in a sequence. Recent research has shown that increasing model size and training data generally improves performance on standard benchmarks, with models like GPT-4 achieving over 90% accuracy on many academic and professional tests (though these scores require careful interpretation). While LLMs have proven effective for many language tasks, controlled studies have documented significant limitations including factual inaccuracies, bias reflection, and inability to truly reason - they fundamentally operate through pattern matching rather than genuine understanding.
+           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>