How to use LLMs for text generation and evaluation

Posted on by Panther

Language Models, also known as LMs, are a fundamental tool in Natural Language Processing (NLP) tasks such as text generation, machine translation, and speech recognition. Recently, there has been a lot of excitement around Large Language Models (LLMs) due to their ability to generate coherent and contextually relevant text. In this tutorial, we will explore how to use LLMs for text generation and evaluation.

Table of Contents

  • Introduction to Language Models
  • Overview of Large Language Models (LLMs)
  • Text Generation with LLMs
    • Preparing the LLM
    • Configuring the Text Generation Task
    • Generating Text with LLMs
  • Evaluating Text Generated by LLMs
    • Intrinsic Evaluation
    • Extrinsic Evaluation
  • Conclusion

Introduction to Language Models

Language Models are statistical models that assign probabilities to sequences of words in a given language. They are trained on large amounts of text data and learn the probabilities of words or word sequences based on their context. For example, given the sentence “I love to eat ___,” a language model can predict the most probable word to complete the sentence, such as “pizza.”

Language Models are typically evaluated based on their ability to predict the next word in a sequence given the context. The perplexity metric is commonly used to measure the quality of a language model. A lower perplexity indicates a better model that can predict the next word more accurately.

Overview of Large Language Models (LLMs)

Large Language Models (LLMs) are a class of language models that have been trained on vast amounts of data, often consisting of billions of words or more. These models use architectures like transformer networks that allow them to learn long-range dependencies and capture the nuances of a given language.

LLMs have shown impressive capabilities in generating coherent and contextually relevant text. They can generate realistic sentences, paragraphs, and even whole articles. The high quality of text generated by LLMs has sparked interest and excitement in various fields, including creative writing, content generation, and chatbots.

Text Generation with LLMs

Text generation with LLMs involves providing a prompt or a starting point to the model and asking it to generate text that follows the given context. The generated output can be anything from a single word to several paragraphs, depending on the desired task.

Preparing the LLM

To use a pre-trained LLM, you need to have the appropriate software libraries installed. The most popular libraries for working with LLMs are Hugging Face’s transformers and OpenAI’s GPT.

Using the transformers library, you can easily load a pre-trained LLM:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

Here, we are using the GPT-2 model, which is one of the widely used LLMs. You can experiment with different models depending on your requirements.

Configuring the Text Generation Task

Before generating text with the LLM, you need to configure the task using specific parameters:

  • Prompt: The starting point or context for text generation. It can be a single word, a sentence, or a longer text.
  • Length: The number of words or tokens you want the generated text to be.
  • Temperature: A parameter that controls the randomness of the generated text. Lower temperature values like 0.1 make the output more deterministic, while higher values like 1.0 add more randomness.
  • Top-k: The number of most probable words to consider at each step. Setting a higher value will make the generated text more diverse.
  • Top-p: Also known as nucleus sampling, this parameter considers the cumulative probability only until the chosen threshold. Higher values like 0.9 focus on a smaller set of high probability words.

Generating Text with LLMs

Once the LLM is configured, you can use it to generate text by providing a prompt:

prompt = "Once upon a time"
input_ids = tokenizer.encode(prompt, return_tensors='pt')

output = model.generate(input_ids, max_length=100, temperature=0.7, top_k=50, top_p=0.9)

# Decode generated output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

In this example, we set the maximum length of the generated text to 100 words. The temperature, top-k, and top-p parameters control the randomness and diversity of the generated output. Experiment with different values to get the desired results.

Evaluating Text Generated by LLMs

Once the text is generated, it is essential to evaluate its quality objectively. Evaluating the quality of generated text can be done using intrinsic and extrinsic evaluation techniques.

Intrinsic Evaluation

Intrinsic evaluation involves measuring the quality of generated text based on its coherence, grammaticality, and overall language fluency. Some commonly used metrics for intrinsic evaluation include:

  • Perplexity: Calculate the perplexity of the generated text by using a language model. Lower perplexity indicates better quality.
  • Distinctness: Measure the distinctiveness of the generated text by counting the percentage of unique words used.
  • n-gram Overlap: Calculate the similarity of the generated text with a reference corpus using n-gram precision, recall, and F1-score.

These metrics can be calculated using available libraries, such as the NLTK library in Python.

Extrinsic Evaluation

Extrinsic evaluation assesses the quality of generated text based on its suitability for a specific downstream task. For example, if the generated text is intended for a chatbot, you can measure the user satisfaction and engagement based on user feedback.

Extrinsic evaluation often involves human judges who assess the generated text based on various criteria. This can be done through user surveys, where participants rate the generated text on aspects like understandability, relevancy, and naturalness.

Conclusion

Large Language Models (LLMs) have revolutionized the field of text generation, providing a powerful tool for generating contextually relevant and coherent text. In this tutorial, we explored how to use LLMs for text generation and evaluation. We learned how to prepare an LLM, configure text generation tasks, and evaluate the quality of generated text. By experimenting with different LLM models and evaluation techniques, you can harness the power of these models for a wide range of applications.

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