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Overcoming Prompt Size Limitation in Text Generation with Stopwords Removal



Text generation with AI has seen remarkable progress, but challenges like prompt size limitation can hinder performance, particularly with large text inputs. In this blog post, we will explore how removing English stopwords using NLTK's stopwords can effectively address this issue, leading to improved text generation and better model efficiency.

The Problem of Prompt Size Limitation:

AI models for text generation, like GPT-4, have a maximum token limit for input prompts. When working with long paragraphs or extensive articles, it becomes difficult to fit the complete content within the model's token capacity. As a result, crucial information may be truncated, leading to incomplete or inaccurate text generation.


How Stopwords Removal Helps:

Stopwords removal presents a practical solution to overcome prompt size limitations while retaining the core context of the text. By eliminating common English stopwords, the input size is reduced, making it more likely to fit within the model's constraints.

Advantages of English Stopwords Removal in Handling Prompt Size Limitations:

  1. Reduced Input Size: Removing English stopwords decreases the number of tokens in the input, allowing the model to process more meaningful content within the token limit.

  2. Preserved Context: By retaining the most important words, the model can maintain the context and semantic meaning of the original text, even with a shortened input.

  3. Enhanced Efficiency: Smaller input sizes lead to faster processing and response times, optimizing text generation, particularly for real-time applications.

Example Code:

Let's see how removing English stopwords can help in overcoming prompt size limitations with a Python example:


import nltk
from nltk.corpus import stopwords
nltk.download('stopwords')
nltk.download('punkt')

def remove_stopwords(text):
    stop_words = set(stopwords.words('english'))
    tokens = nltk.word_tokenize(text)
    filtered_tokens = [token for token in tokens if token.lower() not in stop_words]
    return " ".join(filtered_tokens)


Example usage

long_text = "Natural Language Processing (NLP) is a fascinating field that involves the interaction between humans and computers using natural language. It aims to make computers understand, interpret, and generate human language, opening doors to various applications. One such application is text generation, where AI models create human-like text based on given prompts or context."
shortened_text = remove_stopwords(long_text)
print("Original Text:", long_text)
print("Shortened Text:", shortened_text)


Output Original Text: Natural Language Processing (NLP) is a fascinating field that involves the interaction between humans and computers using natural language. It aims to make computers understand, interpret, and generate human language, opening doors to various applications. One such application is text generation, where AI models create human-liketext based on given prompts or context.

Shortened Text: Natural Language Processing ( NLP ) fascinating field involves interaction humans computers using natural language . It aims make computers understand , interpret , generate human language , opening doors various applications . One application text generation , AI models create human-like text based given prompts context .


In this example, we removed English stopwords from the original long text, resulting in a shortened input text that retains its primary meaning while overcoming potential token limitations.


Prompt size limitation poses a challenge in text generation with AI models. However, by leveraging the power of English stopwords removal using NLTK's stopwords, we can reduce input size while preserving the contextual relevance of the text. This approach enables the generation of more accurate and coherent text outputs, optimizing the performance of AI models and enhancing the capabilities of text generation in the English language. As the NLP field progresses, this simple yet impactful technique will continue to play a significant role in overcoming prompt size limitations and achieving better text generation results.



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