Understanding Pre-Trained Models in Natural Language Processing
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Chapter 1: Introduction to Pre-Trained Models
In the realm of Natural Language Processing (NLP), I frequently utilize Pre-Trained Models (PTMs), particularly Large Language Models (LLMs). Many of us interact with LLMs daily, often unknowingly. For instance, when you engage with virtual assistants like Alexa, Siri, or Google, or use tools like Google Translate, you're relying on these advanced models. Although there has been growing interest in LLMs, I struggled to find a concise guide explaining PTMs in NLP, which prompted me to write this article. Here, I will elucidate what LLMs are, clarify the concept of "pre-training," and provide examples of the processes involved in developing LLMs, including both pre-training and fine-tuning for various downstream applications such as machine translation, question answering, and named-entity recognition.
Section 1.1: Defining Large Language Models
Large Language Models (LLMs) are designed to process extensive text datasets without prior knowledge of specific tasks—this initial phase is known as "Pre-Training." The fundamental principle is that the model absorbs a vast quantity of data to extract "general knowledge." This differs from traditional machine learning methods, where we explicitly instruct the model on tasks. During "pre-training," the model learns broadly, and once this phase concludes, we can guide it on specific tasks in the "Fine-Tuning" stage.
To illustrate the "pre-training" and "fine-tuning" stages, consider a hypothetical scenario. Imagine a person starting with no knowledge. We provide them with numerous books in both English and French, without any instructions. Eventually, this individual discovers they can read both languages, acquiring general knowledge from their studies—this mirrors the "pre-training" of a large language model. Assuming this person has an excellent memory, they retain information about both languages. Next, we present them with a curated dataset containing English sentences paired with their French translations—this represents the "fine-tuning" phase. Initially unaware of the possibility of translation, the individual realizes that their French knowledge corresponds to their English knowledge, allowing them to translate between the two languages. This revelation is powerful in LLMs; through exposure to limited examples, they can generalize and perform specific tasks.
Section 1.2: Overview of Pre-Training and Fine-Tuning
At this juncture, you might wonder how this process differs from typical ML training methodologies. Let's briefly revisit the various training types commonly employed in ML.
- Supervised Learning: This involves training with labeled data. For instance, in machine translation, labeled data consists of the original language paired with its translation.
- Unsupervised Learning: In this approach, the training data lacks labels. For machine translation, this would mean using monolingual text, similar to our hypothetical individual with only monolingual books.
- Self-Supervised Learning: This combines aspects of supervised and unsupervised methods, generating labels automatically.
As we explored LLMs, we noted that they leverage large amounts of unlabeled data during the "pre-training" phase (akin to unsupervised learning) and a smaller labeled dataset in the "fine-tuning" phase (similar to supervised learning).
Chapter 2: Why Choose LLMs?
There are two primary reasons we opt for LLMs rather than constructing a machine learning model from scratch for various NLP tasks.
- Data Availability: Deep learning models, particularly neural networks, are promising architectures in NLP due to their complexity and numerous parameters. However, they require substantial labeled training data to prevent overfitting. Acquiring labeled data is costly, as it necessitates human input. Conversely, unlabeled data is widely accessible online, such as Wikipedia and various monolingual texts. Consequently, NLP researchers decided to divide the training process into a "pre-training" phase using unlabeled data, followed by a "fine-tuning" phase with smaller labeled datasets for specific downstream tasks.
- Scalability: This two-step approach provides a modular structure, allowing a single LLM to be fine-tuned for multiple tasks. For example, one LLM can be adapted for intent classification and subsequently fine-tuned for named-entity recognition.
Section 2.1: Examples of Downstream Tasks
In this section, I will outline some popular downstream tasks that LLMs can be fine-tuned for, followed by a hands-on experience with these tasks using LLMs!
- Machine Translation: This is straightforward; the technology behind Google Translate exemplifies machine translation.
- Named-Entity Recognition: This model identifies and categorizes entities in a text, such as names, locations, and organizations. This can be beneficial for companies sifting through large datasets to extract specific information.
- Machine Reading Comprehension: This model answers questions based on a given document. It can read a text and respond to user inquiries with information derived from that text.
- Sentiment Analysis: This involves classifying the sentiment expressed in text as positive, negative, or neutral.
- Summarization: This task generates concise summaries from lengthy texts. For instance, a customer service representative could benefit from an LLM-generated summary of a customer's case history, saving time for both the representative and the customer.
For those interested in practical applications of these downstream tasks, stay tuned for upcoming posts!
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