Natural Language Models: From BERT to GPT – Large Language Models Explained

Large language models trained on massive text corpora have achieved remarkable capabilities in understanding and generating human language. These models learn statistical patterns about language from billions of parameters trained on vast datasets. Pre-training on general text followed by task-specific fine-tuning has become the dominant paradigm. Models like BERT, GPT, and specialized variants demonstrate that scale enables capabilities emergent from smaller models. Understanding these systems and their capabilities and limitations is essential for responsible deployment.

What is Models?

Language Benefits

Transformer architecture revolutionized natural language processing by replacing recurrent networks with attention mechanisms. Self-attention enables processing of entire sequences in parallel rather than sequentially. Attention mechanisms learn which parts of input are relevant for computing outputs. Positional encoding provides sequence order information. Multi-head attention enables learning multiple types of dependencies simultaneously. Transformer efficiency and parallelizability enabled scaling to larger models and datasets.

What is Language?

These Benefits

Tokenization converts text into discrete tokens enabling processing by neural networks. Character-level tokenization preserves individual characters. Word-level tokenization treats complete words as tokens. Subword tokenization balances vocabulary size and coverage. Special tokens mark sequence starts, ends, and padding. Tokenization choices affect model capability and computational efficiency.

Models Benefits

Embedding spaces represent words and tokens as vectors capturing semantic meaning. Static embeddings assign fixed vectors to words regardless of context. Contextual embeddings vary based on surrounding text. Word similarities are reflected in vector distances. These embeddings enable transfer of learned patterns across tasks and domains.

What is Training?

Training Benefits

Pre-training objectives determine what linguistic capabilities models learn. Masked language modeling predicts masked words from context. Causal language modeling predicts next words given previous context. Span corruption reconstruction of corrupted text spans. These objectives induce learning of grammar, factual knowledge, and reasoning skills.

What is These?

Information Benefits

BERT and encoder-only models focus on understanding tasks. Bidirectional attention enables using both previous and future context. Fine-tuning on downstream tasks produces strong classification and tagging performance. However, these models don’t naturally support generation tasks. Applications include sentiment analysis, named entity recognition, and question answering.

Language Benefits

GPT and decoder-only models focus on generation tasks. Unidirectional attention restricts information flow enabling generation. Training on massive datasets enables strong language understanding as emergent consequence of generation training. Few-shot learning enables task adaptation through prompting without fine-tuning. Remarkable abilities emerge from scaling including reasoning, planning, and explanation generation.

What is Information?

These Benefits

T5 encoder-decoder models provide flexibility for diverse tasks. Encoder processes input context. Decoder generates outputs. A single model architecture handles translation, summarization, question answering, and generation. Unified text-to-text framework simplifies training and application. These models balance the advantages of encoder-only and decoder-only approaches.

Models Benefits

Prompt engineering has become essential art for effectively using large language models. Careful instruction phrasing significantly affects model outputs. Chain-of-thought prompting improves reasoning by encouraging step-by-step thinking. Few-shot examples provide context for desired outputs. Role specification like “you are a teacher” influences response style. Prompt optimization enables accessing model capabilities without fine-tuning.

Training Benefits

In-context learning enables models to rapidly adapt to new tasks from examples in prompts. Few examples of input-output pairs enable the model to infer the task. Zero-shot learning generates reasonable outputs without examples based on task description alone. This capability enables flexible application to novel tasks without retraining.

Information Benefits

Instruction following in large language models enables natural language requests. Models trained on instruction-following datasets respond helpfully to natural language instructions. Constitutional training provides guidelines about helpful, harmless, and honest responses. RLHF (Reinforcement Learning from Human Feedback) aligns model responses with human preferences. These techniques make models more usable and safer.

Language Benefits

Factuality and hallucination remain challenges in language generation models. Models may generate plausible-sounding but false information. Hallucinations occur more in long generations and with less familiar topics. Retrieval-augmented generation combines generation with knowledge retrieval improving factuality. Fact-checking and verification mechanisms reduce false information. Acknowledging uncertainty when appropriate improves trustworthiness.

These Benefits

Summarization from documents leverages language models’ ability to extract and compress key information. Abstractive summarization generates novel text capturing essential content. Extractive summarization selects important sentences. Query-focused summarization concentrates on information relevant to questions. Summarization reduces information overload enabling rapid understanding of lengthy content.