AI Reference 048 Large Language Models Wikipedia guided Primary sources linked

Fine-Tuning Language Models

Definition

Fine-tuning continues training a pretrained model on a narrower dataset or objective so it performs better for a particular domain, task or behavior.

What is fine-tuning?

Fine-tuning continues training a pretrained model on a narrower dataset or objective. Instead of learning language from scratch, the model starts from general representations acquired during pretraining and adjusts them for a domain, task or behavior.

Common forms

Supervised fine-tuning uses examples of desired inputs and outputs. Parameter-efficient methods such as LoRA modify a smaller set of added or selected parameters rather than updating every weight. Domain fine-tuning may use specialist text such as code or scientific material.

When fine-tuning is useful

Fine-tuning is useful when prompting alone does not reliably produce the required behavior or when a model needs a consistent domain-specific pattern. It is not automatically the right solution for changing facts: retrieval is often better when the underlying information changes frequently.

Related terms, defined

Large Language Models RLHF and Preference Training Reinforcement learning from human feedback uses preference information from people to train or guide models toward preferred behavior; modern post-training also uses related preference-optimization techniques. Large Language Models Tokens in AI Tokens are the discrete units processed by a language model. Depending on the tokenizer, a token can represent a word, part of a word, punctuation, whitespace patterns or another text fragment. Large Language Models Tokenization Tokenization converts text into token identifiers a model can process. Model families use different tokenizers, so the same text can consume different numbers of tokens in different systems. Large Language Models Context Windows A context window is the amount of tokenized information a model can consider in one working sequence or request. Larger context increases capacity for long inputs but does not guarantee perfect retrieval or reasoning over every detail. Large Language Models Embeddings in Modern AI Embeddings are dense numerical vectors used to represent text, images or other objects in spaces where distance or similarity can be computed.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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