Text Processing Capabilities and Application Examples of Large Language Models

Large language models excel at converting input text into different formats, such as language translation, grammar correction, and format conversion (e.g., HTML to JSON). Compared to traditional regular expression methods, using large models with prompt words can achieve text processing tasks more simply and efficiently. The following examples demonstrate their application scenarios.

6.1 Translation Tasks

Trained on multilingual text, large language models possess translation capabilities and can handle hundreds of languages.Example 1:Prompt: Translate the following English text into Spanish.Input: Hi, I would like to order a blender.Output: Hola, me gustaría ordenar una licuadora.

Example 2:Prompt: Tell me what language this is.Input: Combien coûte la lampe d’air (French).Output: The model identifies it as French.

6.2 Universal Translator

Models can handle multi-language translation simultaneously and adjust tone according to context (e.g., formal/informal).Example:Prompt: Translate "I would like to order a basketball" into French, Spanish, and English.(Output omitted here; the model generates translations in the corresponding languages.)

Formal and Informal Tone Translation:Prompt: Translate "Would you like to order a pillow？" into formal and informal Spanish.Note: Use clear delimiters (e.g., quotation marks) to separate inputs. The model generates different tone translations based on context (formal for professional settings, informal for friends).

6.3 Tone Conversion

Suppose you are the head of a multinational e-commerce company and need to handle IT problem messages in multiple languages.Process:

1. Input a list of multilingual messages and process each message in a loop.
2. First ask the model to identify the language, then translate it into English and Korean.Optimized Prompt: For concise feedback, request "only answer the language name" or "return in JSON format."

6.4 Format Conversion

Slang to Business Correspondence

Example:Input: "Bro, this is Joe. Check out the specs for this floor lamp."Output: The model generates a formal business letter: "Proposal Regarding Floor Lamp Specifications."

JSON to HTML

Example:Prompt: Convert a JSON list of restaurant staff names and emails into an HTML table with column headers and a title.Code Logic:

python

`# Assume input JSON data
data = [{"name": "John", "email": "[email protected]"}, ...]

Call the model to generate HTML and print

html = getCompletion(f"Convert the following JSON to an HTML table: {data}")
display(html) # Use a Python library to display HTML`

Output: Generates a properly formatted HTML table.

6.5 Grammar and Spelling Checking

Basic Proofreading

Example:Input: A list of sentences with grammatical errors (e.g., "He walk very fast").Output: The model corrects it to "He walks very fast."

Advanced Proofreading (APA Style)

Prompt: Proofread a review, make it more engaging, follow APA style, and output in Markdown.Input: A review about "stuffed pandas" (original text omitted).Output: The model generates an extended APA-style review formatted in Markdown.

Tool Assistance:Use the Python RedLines package to compare the original text with the corrected version and visually show differences.

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The above content demonstrates the application of large models in translation, format conversion, grammar checking, etc. Flexible adaptation to different needs can be achieved through reasonable prompt design.