Applications of LLMs with Examples

Applications of LLMs

Applications of Large Language Models (LLMs)

LLMs like GPT-4, BERT, and T5 power a wide range of applications in natural language processing. Below are some of the most common ones with Python examples.

1. Chatbots (e.g., GPT-powered)

LLMs like GPT-4 can carry out human-like conversations, making them ideal for virtual assistants, customer support, and interactive bots.

import openai

openai.api_key = "your-api-key"

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the weather like today in New York?"}
    ]
)

print(response['choices'][0]['message']['content'])

2. Content Generation

LLMs can generate coherent articles, stories, emails, and marketing copy based on a prompt.

from transformers import pipeline

generator = pipeline("text-generation", model="gpt2")
result = generator("Write a short story about a robot who learns to love:", max_length=50)
print(result[0]['generated_text'])

3. Summarization

Summarization condenses long text into a shorter version, preserving key ideas. T5 and BART models are commonly used for this.

from transformers import pipeline

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
text = """
Artificial Intelligence is changing the way we live. From personalized recommendations to autonomous cars,
AI is improving efficiency across industries. However, ethical concerns remain important.
"""

summary = summarizer(text, max_length=50, min_length=25, do_sample=False)
print(summary[0]['summary_text'])

4. Translation

LLMs like T5 or MarianMT can be used to translate text between languages.

from transformers import MarianMTModel, MarianTokenizer

model_name = "Helsinki-NLP/opus-mt-en-fr"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)

text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt", padding=True)
translated = model.generate(**inputs)
print(tokenizer.decode(translated[0], skip_special_tokens=True))

5. Sentiment Analysis

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