Beginner Explanation
Imagine you have a really smart robot friend who has read millions of books, articles, and websites. This robot can understand what you say and can also write stories, answer questions, or chat with you just like a human. This robot is like a large language model! It learns from all the text it has read and tries to predict what words should come next in a sentence, making it very good at talking and writing.Technical Explanation
Large Language Models (LLMs) are sophisticated neural networks, often based on architectures like Transformers, trained on extensive corpuses of text data. They use self-attention mechanisms to weigh the relevance of different words in a sentence. For example, using the Hugging Face Transformers library, you can load a pre-trained model and generate text as follows: “`python from transformers import GPT2LMHeadModel, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained(‘gpt2’) model = GPT2LMHeadModel.from_pretrained(‘gpt2’) input_text = ‘Once upon a time’ input_ids = tokenizer.encode(input_text, return_tensors=’pt’) output = model.generate(input_ids, max_length=50) print(tokenizer.decode(output[0], skip_special_tokens=True)) “` This code snippet demonstrates how to generate a continuation of a given text using a pre-trained GPT-2 model, showcasing the capabilities of LLMs in text generation.Academic Context
Large Language Models are rooted in the field of Natural Language Processing (NLP) and are primarily based on the Transformer architecture introduced in the paper ‘Attention is All You Need’ by Vaswani et al. (2017). These models leverage self-attention mechanisms, allowing them to capture long-range dependencies in text. The training process involves unsupervised learning on large text datasets, optimizing for the likelihood of predicting the next word in a sequence. Key papers include ‘BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding’ by Devlin et al. (2018) and ‘Language Models are Few-Shot Learners’ by Brown et al. (2020), which discuss advancements in model architectures and training techniques.Code Examples
Example 1:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
input_text = 'Once upon a time'
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))Example 2:
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
View Source: https://arxiv.org/abs/2511.16652v1