OpenAI’s “Chat” and “Text completion” are two different ways of using their language models to generate or manipulate text. I will explain the main differences between them and give some examples of how they can be used.
“Chat” models are designed to generate conversational texts, such as dialogues, chats, emails, etc. They take a series of messages as input, and return a model-generated message as output. For example, if you give the API the prompt:
Preview You are a helpful assistant. User: Who won the world series in 2020? Assistant: The Los Angeles Dodgers won the World Series in 2020. User: Where was it played?
The model will return something like:
Assistant: The 2020 World Series was played in Arlington, Texas at the Globe Life Field, which was the new home stadium for the Texas Rangers.
Advantage of Chat
The main advantage of chat is that it makes multi-turn conversations easy. You don’t have to worry about formatting your input and output as strings. You can also leverage the conversation history to provide context and continuity for the model. The model will try to maintain a consistent persona and tone based on the system message and the prior messages.
Also “Chat” models can generate natural and fluent texts that follow the tone and style of the conversation. They can also handle questions and answers, and refer to previous messages in the conversation.
Disadvantage of Chat
The disadvantage of using “Chat” models is that they may not be very accurate or consistent with factual information. They may also generate texts that are irrelevant or inappropriate for the translation task.
The main challenge of chat is that it requires more data and processing power than text completion. You have to provide enough messages to establish a clear goal and scenario for the conversation. You also have to deal with the token limit of the model, which means you may have to shorten or truncate long conversations. The model may also generate responses that are irrelevant, inconsistent, or inaccurate.
“Text completion” models are designed to generate or manipulate texts based on a given prompt. They provide a simple but powerful interface to any of the OpenAI models. You input some text as a prompt, and the model will generate a text completion that attempts to match whatever context or pattern you gave it. For example, if you give the API the prompt:
As Descartes said, I think, therefore
The model will return something like:
Advantage of Text completion
The main advantage of text completion is that it gives you full control over the input and output. You can design your own prompts and instructions to guide the model to do what you want. You can also adjust the settings such as temperature and top_p to control how deterministic or creative the model is in generating a response.
using “Text completion” models can generate texts for a wide variety of tasks, such as summarization, paraphrasing, rewriting, etc. They can also handle complex text analysis and generation tasks, such as ranking, classification, sentiment analysis, etc.
Disadvantage of Text completion
The disadvantage of using “Text completion” models is that they may not be very natural or fluent in generating texts. They may also generate texts that are incomplete or inconsistent with the prompt.
The main challenge of text completion is that it requires careful prompt design and quality data. You have to be explicit and clear in describing what you want, either through instructions, examples, or a combination of the two. You also have to provide enough examples or context for the model to follow a pattern or learn a task. The model’s performance may vary depending on the complexity of the task and the quality of your prompt.
Example Completion Chat vs Text completion
To illustrate the difference between “Chat” and “Text completion” models, let’s look at some examples of how they would translate a sentence from English to French.
English: How are you today?
French (Chat): Comment allez-vous aujourd’hui?
French (Text completion): Comment vas-tu aujourd’hui?
In this example, both models produce correct translations, but with different levels of formality. The “Chat” model uses the formal “vous” pronoun, while the “Text completion” model uses the informal “tu” pronoun. This shows that the “Chat” model is more sensitive to the tone and style of the conversation, while the “Text completion” model is more generic and simple.
English: The capital of France is Paris.
French (Chat): La capitale de la France est Paris.
French (Text completion): Paris est la capitale de la France.
In this example, both models produce correct translations, but with different word orders. The “Chat” model preserves the word order of the original sentence, while the “Text completion” model changes it. This shows that the “Chat” model is more faithful to the source text, while the “Text completion” model is more flexible and creative.