A course [largely on bibliographic] information managemet

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This is a 6 cr. course for the students of the grades on Humanities and Modern Languages at the University of Deusto (Bilbao, Spain)

A course on
Information
Management
Joseba Abaitua
(2015)
Departamento de Lenguas Modernas y Estudios Vascos
Facultad de Ciencias Sociales y Humanas
Universidad de Deusto

A course on
(largely bibliographic)
Information
Management
Joseba Abaitua
(2015)
Departamento de Lenguas Modernas y Estudios Vascos
Facultad de Ciencias Sociales y Humanas
Universidad de Deusto

because we not only
suffer from
information
overload,
but also...

not every published
information is reliable

A course [largely on bibliographic] information managemet

$Alex Verstak and [[Anurag Acharya]],<ref>{{cite doi|10.1038/438554a}}</ref> both of whom were then working on building Google's main web index.<ref> {{cite web |author=Hughes, Tracey |title=An interview with Anurag Acharya, Google Scholar lead engineer |date=December 2006 |work=Google Librarian Central |url=http://www.google. com/librariancenter/articles/0612_01.html}} </ref><ref>{{cite web |author=Assisi, Francis C. |title=Anurag Acharya Helped Google’s Scholarly Leap |date=3 January 2005 |publisher=INDOlink |url=http://www.indolink.com/SciTech/fr010305- 075445.php}}</ref>$

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$<ref> {{cite web | author=Hughes, Tracey | title=An interview with Anurag Acharya, Google Scholar lead engineer | date=December 2006 | work=Google Librarian Central | url=http://www.google.com/librariancenter/articles/0612_01.html }} </ref>$

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**The Rise and Impact of Large Language Models (LLMs)** **Introduction** In the rapidly evolving landscape of artificial intelligence (AI), one of the most groundbreaking advancements has been the development of Large Language Models (LLMs). These AI systems, trained on massive amounts of text data, have demonstrated remarkable capabilities in understanding, generating, and manipulating human language. LLMs have transformed industries, reshaped the way people interact with technology, and raised ethical concerns regarding their usage. This essay delves into the history, development, applications, challenges, and future of LLMs, providing a comprehensive understanding of their significance. **Historical Background and Development** The foundation of LLMs is built on decades of research in natural language processing (NLP) and machine learning (ML). Early language models were relatively simple and rule-based, relying on statistical methods to predict word sequences. However, the emergence of deep learning, particularly the introduction of neural networks, revolutionized NLP. The introduction of recurrent neural networks (RNNs) and long short-term memory (LSTM) networks in the late 1990s and early 2000s allowed for better sequential data processing. The breakthrough moment for LLMs came with the development of Transformer architectures, introduced in the seminal 2017 paper "Attention Is All You Need" by Vaswani et al. The Transformer model enabled more efficient parallel processing and improved context understanding. This led to the creation of models like BERT (Bidirectional Encoder Representations from Transformers) and OpenAI’s GPT (Generative Pre-trained Transformer) series, which have since set new benchmarks in AI-driven text generation and comprehension. **Core Mechanisms of LLMs** LLMs rely on deep neural networks trained on extensive datasets comprising books, articles, websites, and other textual resources. The training process involves: 1. **Tokenization:** Breaking down text into smaller units (words, subwords, or characters) to be processed by the model. 2. **Pretraining:** The model learns general language patterns through unsupervised learning, predicting missing words or the next sequence in a text. 3. **Fine-tuning:** Adjusting the model for specific tasks, such as summarization, translation, or question-answering, using supervised learning. 4. **Inference:** The trained model generates text based on user input, leveraging probabilistic predictions to produce coherent responses. Through these mechanisms, LLMs can perform a wide range of linguistic tasks with human-like proficiency. **Applications of LLMs** LLMs have found applications across various domains, including but not limited to: 1. **Content Generation:** LLMs assist in writing articles, blogs, poetry, and even code, helping content creators enhance productivity. 2. **Customer Support:** Chatbots and virtual assistants powered by LLMs provide automated yet cont

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A course [largely on bibliographic] information managemet

1. A course on Information Management Joseba Abaitua (2015) Departamento de Lenguas Modernas y Estudios Vascos Facultad de Ciencias Sociales y Humanas Universidad de Deusto

2. A course on (largely bibliographic) Information Management Joseba Abaitua (2015) Departamento de Lenguas Modernas y Estudios Vascos Facultad de Ciencias Sociales y Humanas Universidad de Deusto

3. Why do we need to ‘manage’ information?

4. because we not only suffer from information overload, but also...

5. not every published information is reliable

6. we need to be selectiveselective

36. Alex Verstak and [[Anurag Acharya]],<ref>{{cite doi|10.1038/438554a}}</ref> both of whom were then working on building Google's main web index.<ref> {{cite web |author=Hughes, Tracey |title=An interview with Anurag Acharya, Google Scholar lead engineer |date=December 2006 |work=Google Librarian Central |url=http://www.google. com/librariancenter/articles/0612_01.html}} </ref><ref>{{cite web |author=Assisi, Francis C. |title=Anurag Acharya Helped Google’s Scholarly Leap |date=3 January 2005 |publisher=INDOlink |url=http://www.indolink.com/SciTech/fr010305- 075445.php}}</ref>

37. <ref>{{cite doi|10.1038/438554a}}</ref> <ref>{{cite web |author=Hughes, Tracey |title=An interview with Anurag Acharya, Google Scholar lead engineer |date=December 2006 |work=Google Librarian Central |url=http://www.google. com/librariancenter/articles/0612_01.html}}</ref> <ref>{{cite web |author=Assisi, Francis C. |title=Anurag Acharya Helped Google’s Scholarly Leap |date=3 January 2005 |publisher=INDOlink |url=http://www.indolink.com/SciTech/fr010305- 075445.php}}</ref>

38. <ref> {{cite web | author=Hughes, Tracey | title=An interview with Anurag Acharya, Google Scholar lead engineer | date=December 2006 | work=Google Librarian Central | url=http://www.google.com/librariancenter/articles/0612_01.html }} </ref>

39. {{Infobox website | name = Google Scholar | logo = [[File:Google Scholar logo.png]] | caption = Google Scholar logo | url = {{URL|scholar.google.com}} | commercial = | type = [[bibliographic database]] | language = | registration = Optional | owner = [[Google]] | launch date = | current status = Active | revenue = }}

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