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Paradigm Shift in Natural Language Processing
Before the widespread adoption of very large, general-purpose language models, a common approach to solving a specific natural language processing task (like sentiment analysis or machine translation) was to train a model exclusively on a dataset curated for that single task. Contrast this traditional, task-specific approach with the modern approach. In your answer, analyze the key differences in terms of (1) the training objective and data, and (2) how the model is applied to solve a variety of tasks.
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Data Science
Deep Learning (in Machine learning)
Collective Intelligence
Psychology
Social Science
Empirical Science
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Foundations of Large Language Models
Ch.2 Generative Models - Foundations of Large Language Models
Ch.3 Prompting - Foundations of Large Language Models
Analysis in Bloom's Taxonomy
Cognitive Psychology
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Transforming NLP Tasks into Text Generation with LLMs
Generative LLMs as a Focus of Study
Core Topics in LLM Development and Scaling
Interchangeable Use of 'Word' and 'Token' in Language Modeling
Comparison of Traditional vs. Modern Language Model Applications
Power and Cost of Large Language Models
Modern View on Continued Performance Gains from Scaling
Rapid Evolution and Research Landscape of LLMs
Next-Token Prediction as the Training Objective for LLMs
Shift in Perspective on Language Modeling's Role in AI
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LLM Training and Fine-Tuning
A technology firm needs to build systems for three different language-based tasks: summarizing long articles, translating user interface text, and answering frequently asked questions. They are evaluating two approaches. Approach 1 involves building a single, very large system trained on a vast and diverse collection of text from the internet, with the simple objective of learning to predict the next piece of text in a sequence. This one system would then be guided to perform all three tasks. Approach 2 involves developing three separate, specialized systems, each trained exclusively on a dataset tailored to one specific task (e.g., a dataset of article-summary pairs for the summarization system). Which statement best analyzes the core principle that distinguishes these two approaches?
High Cost of Building LLMs
Choosing the Right NLP Approach for a Specialized Task
Paradigm Shift in Natural Language Processing
Solving Difficult NLP Problems with LLMs
LLM-Powered Conversational Systems
Dimensions of Large Language Models: Depth and Width