Use LLMs for Natural Language Processing (NLP) tasks

  • 9 minutes

Common Natural Language Processing (NLP) tasks harness the capabilities of Large Language Models (LLMs) to address a wide range of language-related challenges.

These tasks include:

  • Summarization: Condense lengthy texts into concise summaries.
  • Sentiment analysis: Identifies the emotional tone of text.
  • Translation: Convert text between languages.
  • Zero-shot classification: Categorize text into predefined labels without prior examples.
  • Few-shot learning: Adapt to new tasks with minimal training data.

These applications demonstrate the transformative potential of LLMs in processing and understanding human language.

Let's explore each of these tasks in more detail.

Summarize text

Summarization is a common NLP task where a language model condenses a lengthy piece of text into a shorter version while preserving the key information and main ideas.

There are two types of summarization:

  • Extractive summarization involves selecting important sentences or phrases directly from the source text.
  • Abstractive summarization generates new sentences that capture the essence of the original text.

LLMs, with their advanced understanding of context and language, excel in abstractive summarization, producing coherent and contextually accurate summaries.

You can use summarization in various applications, like generating news briefs, summarizing research papers, and creating concise reports.

Illustration of a news article that is summarized for a phone feed.

Perform sentiment analysis

Sentiment analysis, also known as opinion mining, is the process of determining the emotional tone behind a body of text.

Sentiment analysis involves classifying the text into categories such as positive, negative, or neutral sentiment.

LLMs are highly effective at this task due to their ability to understand nuanced language and context. By analyzing word choice, sentence structure, and context, these models can accurately gauge the sentiment expressed in reviews, social media posts, customer feedback, and more.

Sentiment analysis is widely used in businesses to monitor brand reputation, gauge customer satisfaction, and understand public opinion on various topics.

Diagram of two examples for sentiment analysis.

Translate text

Translation is the task of converting text from one language to another, and LLMs revolutionized this field with their ability to perform high-quality machine translation.

These language models use vast multilingual datasets and sophisticated neural network architectures to understand and generate text in multiple languages. LLMs can capture the nuances and idiomatic expressions of different languages, and produce translations that aren't only accurate but also contextually appropriate.

Compared to previous techniques for translation, LLMs are often more accurate as they can understand the semantic meaning of a text before translating it, which results in less literal translations.

Machine translation powered by LLMs is essential for global communication, enabling businesses, governments, and individuals to interact across language barriers with greater ease and accuracy.

Use zero-shot classification

Zero-shot classification is a technique where an LLM can categorize text into predefined labels without seeing any labeled examples during training.

Zero-shot classification is achieved by using the model's extensive general knowledge and language understanding.

You provide a natural language description of the labels, and the model classifies the text based on this input.

Zero-shot classification is highly versatile and efficient, as it eliminates the need for large, labeled training datasets specific to each new task. This capability is useful in fields where new categories emerge frequently, allowing for immediate adaptation and application.

Diagram of two examples for zero-shot classification.

Use few-shot learning

When you use few-shot learning, you provide an LLM with a few examples before it performs a specific task.

Providing a model with a few examples, enables the model to quickly adapt to new tasks with minimal data, using its established language knowledge.

Few-shot learning is advantageous in scenarios where labeled data is scarce or expensive to obtain. By providing a handful of examples, you can guide the model to perform specialized tasks such as domain-specific text classification, custom sentiment analysis, or entity recognition.

The flexibility and efficiency make few-shot learning a powerful tool for deploying LLMs in diverse real-world applications.

For example, explore the following prompt that includes few-shot learning:

## Instructions
For each tweet, describe its sentiment.

## Examples 
Tweet: I hate it when my phone battery dies
Sentiment: Negative

Tweet: My day has been great
Sentiment: Positive

Tweet: This is the ink to the article
Sentiment: Neutral

Tweet: This new music video is incredible
Sentiment: Positive

The LLM uses the examples to understand what it needs to do and completes the prompt by returning the sentiment of the last tweet.

Now that you explored various tasks for LLMs, you can use Azure Databricks to experiment with open-source LLMs and test their capabilities with prompts.