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

Versatility and Generalization of LLMs

Soft Prompting

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

Galactica

OPT (Open Pretrained Transformers)

BLOOM

Llama 1

Emergent Abilities of Large Language Models

BIG-Bench Benchmark

Visual Language Model