GPT-4

As explained in Data Analysis with LLMs

GPT-4 is a GPT language model version capable of generating and understanding text, including complex code.

GPT-4 is a large GPT language model for processing text, including tasks such as classifying reviews and writing complex data-processing code. Its stronger capabilities come with substantially higher per-token costs, so model choice should match the task. section 1.5.1 section 9.1

Use GPT-4 selectively when evaluating language-model options:

• Choose it when a task requires complex code or higher-quality text processing than a smaller model provides. section 1.5.1
• Compare quality against token costs rather than treating the largest model as the automatic default. section 1.5.1 section 9.4
• Test candidate models on representative task data, using benchmarks such as HELM as an initial comparison point. section 1.5.1 section 9.4

Classifying product reviews

A review-classification prompt can provide GPT-4 with one review, instructions, and labels such as “positive” and “negative.” In a comparison with GPT-3.5 Turbo, GPT-4 solved one additional test case accurately, reaching 80% accuracy, but cost roughly 120 times more because of its higher per-token fees. section 9.1 section 9.4

Figure 1.4 Holistic Evaluation of Language Models (HELM): comparing language models offered by different providers according to various metrics

Compare language models from different providers across task metrics with the HELM benchmark. section 1.5.1

How Data Analysis with LLMs covers it

How deep it goes

Explains capabilities, token pricing, model comparison, and task evaluation, but stops short of model internals.

Examples

Uses product-review classification, HELM comparisons, token-cost calculations, and a GPT-4 API invocation.

What it assumes

Assumes familiarity with text-processing tasks and basic model comparison while introducing tokens and per-token pricing.

What you can do after

Evaluate GPT-4 against cheaper models on representative data while weighing accuracy, token use, and price.

How it connects

Connects GPT-4 to language models, tokens, prompt-based classification, benchmarks, and model selection.

liveBookmanning.com

Read Data Analysis with LLMs on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionGPT-4 is an OpenAI GPT language model released in 2023 and capable of performing a range of language and coding tasks.
• 

  liveBookmanning.com
  Elasticsearch in Action, Second EditionGPT-4 is a generative pretrained transformer language model developed by OpenAI.
• 

  liveBookmanning.com
  AI for Everyday ITGPT is OpenAI's family of generative pre-trained transformer language models.
• 

  liveBookmanning.com
  The AI Pocket BookAn OpenAI large language model used as an example tokenizer and reference for token counts.

Compare how 5 books explain GPT-4 →

Related concepts

• multimodalkind of · A multimodal model is a model that processes, generates, or relates information represented in two or more data modalities, such as text, images, audio, or video.
• ChatGPTused by · ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.
• autoregressive modelkind of · An autoregressive model represents or predicts each value in a sequence based on preceding values in that sequence.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• GPT modelcontrasts with · A GPT model is a generative pretrained Transformer language model that produces text autoregressively by predicting successive tokens from preceding context.

This explainer was generated by Manning's knowledge graph from the text of Data Analysis with LLMs. The section links point to the parts of the book it draws on.