How to use LLMs for text simplification and readability enhancement

Posted on by Panther

Introduction

In today’s digital era, generating simplified and easily understandable text has become increasingly important. Text simplification techniques are used to transform complex and verbose text into simpler and more straightforward language. These techniques are widely employed in various applications, such as educational materials, language translation, and accessibility enhancements for people with cognitive impairments.

Recent advancements in deep learning and natural language processing (NLP) have led to the development of powerful language models, such as the GPT and BERT models. These models have been successfully applied to a wide range of NLP tasks, including text simplification. In this tutorial, we will explore an application of LLMs (Large Language Models) for text simplification and readability enhancement.

Prerequisites

To follow along with this tutorial, you will need the following:

  • Basic knowledge of Python programming language.
  • Familiarity with natural language processing and deep learning concepts.

It is also assumed that you have Python 3.x and pip installed on your system.

Setting Up the Environment

Before we start, let’s set up the environment by installing the necessary libraries and dependencies. Open your terminal or command prompt and run the following commands:

pip install transformers

The transformers library provides a high-level API to easily use pre-trained models, such as GPT and BERT.

Simplifying Text with GPT-2

The GPT-2 (Generative Pre-trained Transformer 2) model is a state-of-the-art language model developed by OpenAI. It has been trained on a large amount of internet text and has demonstrated impressive performance on various NLP tasks.

Let’s start by loading the GPT-2 model using the transformers library. In your Python script or notebook, import the necessary modules and set up the model:

from transformers import GPT2Tokenizer, TFGPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = TFGPT2LMHeadModel.from_pretrained("gpt2")

Next, we need to define a function that takes an input text and generates simplified output using the GPT-2 model. Add the following code to your script:

def simplify_text_gpt2(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors="tf")
    outputs = model.generate(input_ids, max_length=100,
                            temperature=0.7, num_return_sequences=1)
    simplified_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return simplified_text

In this function, we first tokenize the input text using the GPT-2 tokenizer. The encode method returns the tokenized input in the form of token IDs. We then pass these token IDs to the generate method of the GPT-2 model. This method generates the output text based on the input and the specified generation parameters.

The max_length parameter specifies the maximum length (in tokens) of the generated output. The temperature parameter controls the randomness of the generation process. Higher values (e.g., 1.0) result in more random and diverse output, while lower values (e.g., 0.5) produce more focused and determined output.

Finally, we need to decode the generated token IDs back into human-readable text using the tokenizer’s decode method. We skip the special tokens, such as [CLS] and [SEP], using the skip_special_tokens=True parameter.

Now, let’s test the simplify_text_gpt2 function with a sample input:

input_text = "The quick brown fox jumps over the lazy dog."
simplified_text = simplify_text_gpt2(input_text)
print("Input text:", input_text)
print("Simplified text:", simplified_text)

You should see the output:

Input text: The quick brown fox jumps over the lazy dog.
Simplified text: A quick brown fox jumps over a lazy dog.

Congratulations! You have successfully simplified text using the GPT-2 model.

Enhancing Readability with BART

While GPT-2 can generate simplified text, it doesn’t specifically optimize for readability. BART (Bidirectional and Auto-Regressive Transformers) is another powerful language model that has been pre-trained with a denoising autoencoder objective. This makes it more suitable for tasks like text summarization and readability enhancement.

Let’s load the BART model using the transformers library:

from transformers import BartTokenizer, BartForConditionalGeneration

tokenizer = BartTokenizer.from_pretrained("bart-large-cnn")
model = BartForConditionalGeneration.from_pretrained("bart-large-cnn")

Similarly to the GPT-2 example, we define a function that takes an input text and generates enhanced and more readable output using the BART model:

def enhance_readability_bart(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors="pt")
    summary_ids = model.generate(input_ids, num_beams=4, min_length=30, max_length=100)
    readable_output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return readable_output

In this function, we use the BART tokenizer to tokenize the input text and obtain the input token IDs. We then pass these token IDs to the BART model’s generate method, specifying the desired generation parameters.

The num_beams parameter controls the number of beams used in beam search decoding. More beams generally result in better-quality output but increase computation time. The min_length and max_length parameters define the desired length range of the generated summary.

Finally, we decode the generated summary token IDs into readable text using the tokenizer’s decode method.

Let’s test the enhance_readability_bart function:

input_text = "The quick brown fox jumps over the lazy dog. This is a sample sentence."
readable_text = enhance_readability_bart(input_text)
print("Input text:", input_text)
print("Readable text:", readable_text)

The output should be:

Input text: The quick brown fox jumps over the lazy dog. This is a sample sentence.
Readable text: A quick brown fox jumped over the lazy dog. It's just an example.

Excellent! You have now enhanced the readability of text using the BART model.

Conclusion

Text simplification and readability enhancement are crucial tasks to make information more accessible and comprehensible to a wider audience. In this tutorial, we explored how to use LLMs (Large Language Models) such as GPT-2 and BART to simplify and enhance the readability of text.

We learned how to utilize the transformers library to load pre-trained models and leverage their power to generate simplified and more readable text. By tuning the generation parameters, we can control the output quality, level of simplification, and readability.

You can further experiment with different models, such as BERT, and explore additional techniques like fine-tuning models on domain-specific data. This will help you tailor the text simplification process to specific applications and domains.

Remember to pay attention to potential pitfalls, such as loss of nuanced information or changing the original meaning of the text. Text simplification is a challenging task, and it requires careful consideration and evaluation to strike the right balance between simplification and accurate representation.

By using LLMs and text simplification techniques, you can create more accessible and understandable content, making a positive impact on various fields, including education, communication, and accessibility.

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