Unpacking the Use of Textual Data in Large Language Models Training

May 31, 2023

With machine learning at their core, Large Language Models can sift through massive data sets. The secret to their success lies in their insatiable appetite for exceptional training data.

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As AI models become increasingly sophisticated and capable of performing a wider range of tasks, the need for high-quality training data has become more critical than ever. With its abundance of texts on social media and news articles, the web has become a rich source of text-based training data for Large Language Models.

Processing ever-growing amounts of texts into structured training data makes it possible to develop models with higher accuracy and robustness. The process itself can be fairly labor-intensive as it comprises several steps collectively known as natural language processing, from entity recognition to relationship extraction, and often enrichment with meta-data like sentiment, relevance, or novelty. Once the training data has been produced, it can be used to train models for a broad range of applications.

Which applications must be trained using textual data?

Text based training data can be used in a variety of contexts, from very basic yet useful applications that we already use in our day to day (take autocomplete for instance) to more advanced ones, that interact with users or draw insights from vast amounts of data. Finally, textual data serves ambitious endeavors, like reinventing search or content generation.

In order to deliver, all these applications need quality textual training data. What does this mean?

High-quality training data is crucial for the creation of sophisticated and reliable language AI systems. Language models must accurately reflect reality in order to be useful; as a result, training data sets that are accurate, varied and with a high degree of specificity are necessary for language models to function well and minimize biases or errors.

To teach the computer how to recognize the outcomes the model is intended to detect, textual training data must be labeled , that is, enriched or annotated.

What is text labeling and why is it important

Text labeling, also known as annotation, is the process of adding descriptive tags or labels to a text dataset to identify specific elements within the text. Imagine you train your model to predict the language of a sentence between Spanish and English. You will need millions of sentences labeled:

  • Hola que tal > Spanish
  • Hi how are you > English
  • Mi casa es roja > Spanish
  • My house is red > English

The labels provide additional information about the content and structure of the text, enabling models to perform a variety of tasks:

Text labeling is important for several reasons

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Improving accuracy of
Large Language Models

Labeled text data is used to train ML models, allowing them to make accurate predictions on new data. The quality of the labeling can greatly impact the performance of the model.

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Understanding Text Content

Labels can be used to identify specific elements within text data, such as sentiment, entities, and topics, providing deeper understanding and insights into the content.

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Automating NLP tasks

Text labeling can be used to automate NLP tasks such as sentiment analysis, named entity recognition, and text classification, freeing up time for more complex and important tasks.

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Data management

Labeling text data helps in organizing and managing large amounts of text data in a structured manner, making it easier to analyze and utilize.

Key takeaways:

  • Textual data is used to train Large Language Models, which can be used for sentiment analysis, topic classification, search, text summarization, among others
  • These applications heavily rely on training data for accuracy and effectiveness
  • To be useful, textual training data must be properly labeled - that is, enriched or annotated - to teach the machine how to recognize the outcomes the model is designed to detect.
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