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Large language models (LLMs) are machine learning models that are really good at comprehending and generating human language.
They have emerged as powerful tools in the field of natural language processing (NLP) and have completely revolutionized the way machines comprehend and generate human language.
These powerful machine learning models have found numerous applications across various industries, transforming the way we interact with technology and process information.
From chatbots to content generation, LLMs have made a significant impact in real-life scenarios.
Let’s dive into more details about LLMs.
In this post, we’ll learn:
Before delving into the concept of a large language model, it is essential to grasp the fundamentals of a language model itself. In simple terms, a language model is a system that understands and predicts human language. It learns patterns and relationships between words and uses that knowledge to generate coherent and meaningful text.
Language modeling helps machines comprehend and generate language, making it essential for various applications like machine translation, chatbots, etc.
In technical terms, a language model refers to a probability distribution over text.
(‘Today is a sunny day.’)
The goal of a language model is to assign higher probabilities to more fluent and coherent sequences while assigning lower probabilities to less likely or nonsensical combinations of words.
(‘Today is a sunny day.’)=0.9
(‘Sunny today is a day.)=0.1
While analyzing large amounts of text data in order to fulfill this goal, language models acquire knowledge about the vocabulary, grammar, and semantic properties of a language. They capture the statistical patterns and dependencies present in a language.
By doing so, a language model can also generate coherent and contextually appropriate text by predicting the likelihood of a particular word given the preceding words.
Effectively, language models are built on the principle that words in a sentence are not chosen independently but rather depend on the words that precede them.
Taking this into account, language models consider the context and order of words to make accurate token predictions.
For instance, in the sentence “The sun is shining,” a language model understands that “shining” is more likely to follow “The sun is” than, say, “playing.”
Language models have experienced recent advancements due to the introduction of advanced neural network architectures, which we will discuss ahead.
However, historically, language models were developed using n-gram models. They were trained to learn and estimate the probability distribution of text based on the frequency of fixed-length sequences of words.
N-gram models have been widely used not just in developing language models but also other NLP models, due to their simplicity and computational efficiency.
Yet, they have limitations in capturing long-range dependencies and suffer from the curse of dimensionality, which makes them less effective in handling larger vocabularies and complex linguistic patterns.
The curse of dimensionality refers to the exponential increase in the number of possible n-grams as the size of the vocabulary and the length of the sequence grows.
This poses challenges in estimating accurate probabilities for rare or unseen n-grams, as the data sparsity increases. N-gram models also struggle to capture context beyond a fixed window of words, limiting their ability to consider broader linguistic contexts and dependencies.
To overcome these limitations, more advanced models, such as recurrent neural networks (RNNs), gained prominence in language modeling.
RNNs, specifically variants like long short-term memory (LSTM) and gated recurrent units (GRU), quickly became capable of capturing sequential dependencies and showed improved performance in language modeling tasks compared to n-gram models.
However, RNNs suffered from difficulties in parallel computation, making them slower and less efficient for longer sequences.
This was resolved with the advent of Transformers, and the field of language modeling witnessed significant advancements.
Transformers are a type of neural network architecture that allows LLMs to process sequential data, such as text, parallelly by considering the context and dependencies between words or tokens.
Unlike traditional recurrent neural networks (RNNs) that process sequential data step-by-step, Transformers leverage a mechanism called self-attention to capture the dependencies between different positions in the input sequence.
This allows them to consider the entire context simultaneously, rather than relying on the sequential processing of data.
The parallelization capabilities of Transformers allow us to scale them effectively and train them on massive text datasets.
With their self-attention mechanism, Transformers could effectively capture dependencies between all positions in the input sequence, regardless of their distance.
This enabled them to model long-range dependencies more effectively and capture global context, resulting in more accurate and coherent language processing and generation.
As a result, language models quickly found applications in a wide range of tasks, including machine translation, speech recognition, text completion, sentiment analysis, and more.
Consequently, they also served as the foundation for large language models (which we will understand shortly). This took language modeling to the next level by incorporating massive amounts of training data. By leveraging powerful computational resources, they achieved unparalleled language understanding and generation capabilities.
Now that we understand the foundational building blocks of a language model, let’s dive into the concept of large language models (LLMs). A large language model refers to a specific type of language model that is characterized by its size, capacity, and ability to comprehend and generate human language at an unfathomable scale.
As discussed above, LLMs are built using deep learning techniques, particularly leveraging Transformer architecture. These models are trained on massive amounts of text data, often encompassing billions or even trillions of words.
That’s where the word “large” comes from in a “large language model”. This includes both the size and complexity of the neural network as well as the size of the dataset it was trained on.
The training process exposes the model to a diverse range of linguistic patterns, contextual information, and semantic relationships present in the data.
With their massive size and extensive training, LLMs excel in understanding the complexities of human language.
Traditionally, in machine learning, we train a model for a specific task, like text sentiment classification or machine translation, etc.
But what makes LLMs especially powerful is that one model can be used for a whole variety of tasks, like chat, copywriting, translation, summarization, brainstorming, code generation, and more.
As discussed above, large language models offer remarkable capabilities in understanding and generating human language.
Recently, they have found numerous applications across various industries, transforming the way we interact with technology and process information.
As they are primarily backed by language, the possibilities of extending them to real-life use cases are endless. Some of them are:
LLMs have greatly enhanced the development of chatbots and virtual assistants, enabling more natural and interactive conversations with users.
LLMs can understand user queries, provide accurate responses, and even engage in contextual dialogues.
LLMs have revolutionized the field of machine translation, breaking down language barriers and facilitating seamless communication across cultures.
They can process and translate text from one language to another while capturing the nuances and context of the original text.
LLMs have become valuable allies for content creators and writers.
They can assist in generating engaging and informative content by offering suggestions, improving grammar and style, and providing topic-specific knowledge.
LLMs can automatically summarize lengthy documents, extract key information, and generate concise summaries.
This application is beneficial for information-intensive domains such as news, research, and legal documents, where quick access to relevant information is essential.
Leveraging the capabilities of LLMs in downstream applications can be significantly helpful. This can enable us to solve a wide range of use cases.
Here are some key strategies for effectively incorporating LLMs in downstream applications:
In this approach, the pretrained language model is used as a feature extractor, and the hidden representations of the model are extracted for each input text.
These representations, also known as embeddings, capture the semantic and contextual information of the input.
Once the embeddings are obtained, they can be fed into a separate task-specific model, such as a classifier or a regressor, which is trained using labeled data specific to the downstream task.
The task-specific model learns to make predictions or perform the desired task using the extracted features from the language model.
The advantage of this approach is that the pretrained language model’s knowledge and understanding of language are effectively transferred to the downstream task without modifying its parameters.
Another approach to using an LLM in downstream applications is by embedding task-specific information in prompts given to an LLM.
This allows us to leverage the pre-trained knowledge of LLMs to tackle new tasks with minimal training data.
Two notable approaches in this direction are:
Zero-shot learning refers to the remarkable ability of LLMs to perform a task for which they have not been explicitly trained.
In other words, zero-shot learning allows the LLM to generate responses or perform specific tasks solely from the instructions in the prompt, without any fine-tuning.
This is possible because, during training, LLMs acquire a broad understanding of language during pre-training and can leverage that knowledge to generalize to new tasks.
For example, an LLM pre-trained on a large corpus of text may be asked to translate between language pairs it has never seen during training, demonstrating impressive zero-shot translation capabilities.
Instead of training the model from scratch, which would require a large labeled dataset, few-shot learning capitalizes on the pretrained knowledge of the LLM to adapt it to new tasks efficiently.
Essentially, by exposing the model to a limited number of task-specific examples in its prompt, it can quickly learn to generate responses or perform tasks with a higher degree of accuracy and fluency.
Few-shot learning is particularly beneficial in scenarios where acquiring large labeled datasets is impractical or expensive. Instead of requiring extensive amounts of task-specific data, LLMs can achieve impressive performance with just a few examples or even a single example per task.
The success of few-shot learning in LLMs can be attributed to the rich knowledge and generalization capabilities acquired during the pre-training phase.
However, the above fine-tuning methodologies, especially zero-shot learning, are not as good as proper fine-tuning with examples.
One potential reason is that it is harder for models to perform well on prompts that are not similar to the format of the pre-training data.
Also, learning without updates requires the model to rely entirely on its existing knowledge.
This makes fine-tuning critical in improving the downstream applicability of an LLM. Thus, let’s understand how LLMs are trained and how to fine-tune them.
Training LLMs is a computationally intensive process that involves two main steps:
The first step in training LLMs is pre-training. During pre-training, the model is exposed to a massive corpus of unlabeled text data, often gathered from the internet.
This unlabeled data serves as the foundation for the model to learn the statistical patterns, semantic relationships, and linguistic structures present in human language. The objective is to enable the model to predict missing words or generate coherent sentences, effectively capturing the statistical patterns in the language.
Pre-training typically involves the use of a language modeling objective, such as masked language modeling or predicting the next word (or sentence) in a sequence.
To facilitate efficient training, distributed computing frameworks and specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs), are employed.
The training process involves optimizing the model’s parameters using stochastic gradient descent (SGD) or similar optimization algorithms, leveraging backpropagation to adjust the model’s weights based on the prediction errors.
After pre-training, the model learns a rich representation of language and acquires knowledge about various linguistic aspects.
However, this pre-trained model still needs to be tweaked to perform specific tasks effectively.
That’s where the fine-tuning comes in.
Fine-tuning is the second step in training LLMs. During this stage, the pre-trained model is further exposed to data specific to a target task.
The objective of fine-tuning is to adapt the pre-trained model’s general language understanding to the specific task at hand.
While pre-training is compute-intensive, fine-tuning can be done comparatively inexpensively. Fine-tuning is more important for the practical usage of such models.
Fine-tuning Large Language Models (LLMs) on specific downstream tasks offers several advantages.
Some key benefits of LLM fine-tuning are:
As discussed above, fine-tuning a language model involves updating the model’s parameters using task-specific labeled data. This approach allows the model to adapt and refine its representations and behaviors to better align with the requirements of the downstream task.
Here’s an overview of the process: Dataset selection: The first step is selecting a task-specific dataset that contains labeled examples relevant to the target task. This dataset should represent the specific patterns, concepts, or behaviors that the model needs to learn.
Task-specific training: The selected dataset is then used to train the LLM with parameter updates. During this phase, the model’s parameters are adjusted.
Updating representations: As the model is fine-tuned on the task-specific dataset, its internal representations and learned features are updated to better capture the patterns and structures specific to the target task.
So far, we have seen how fine-tuning a large language model is a pivotal step in optimizing their performance for specific tasks. However, despite the advancements in fine-tuning techniques, there are inherent challenges involved.
To address them, it is important for humans to intervene, provide feedback and carefully navigate their training.
Some common challenges are:
Fine-tuning relies heavily on task-specific labeled data. However, acquiring a comprehensive and unbiased dataset can be challenging.
What’s more, biases may exist in the collected data. This may lead to inaccurate representations in the fine-tuned model.
The inclusion of human input helps address these challenges. Human annotators provide diverse perspectives, identify biases, and contribute to more balanced and representative datasets, ensuring that the fine-tuned models are more accurate and unbiased.
Language is nuanced, context-dependent, and often ambiguous.
The absence of human input during the fine-tuning process limits the model’s contextual understanding and hinders its ability to generate appropriate responses in complex situations.
Incorporating human expertise reduces this gap. Human annotators provide contextual information, disambiguate ambiguous examples, and impart their understanding of nuanced language use.
LLMs have the potential to impact society significantly. However, fine-tuning without sufficient human oversight may lead to unintended consequences, such as offensive outputs.
However, integrating human input helps us address ethical and social considerations. Human evaluators provide valuable insights into potential biases, identify inappropriate responses, and help fine-tune models to prioritize fairness, inclusivity, and responsible AI practices.
In conclusion, it’s pretty clear that fine-tuning LLMs presents several challenges that can be addressed by incorporating human input.
By leveraging the diverse perspectives of human annotators, we can mitigate the undesired consequences of LLMs.
Human involvement process plays a vital role in unlocking the true potential of LLMs and creating more reliable, accurate, and responsible language models for a wide range of applications.
But getting human input, at scale, is practically challenging.
As discussed above, to be good at a specific task, language models should be fine-tuned with high-quality labeled data and continuous human feedback.
Toloka provides a global platform for human-driven data labeling at all stages of training generative models (like LLMs), at scale. We offer custom solutions for:
With a global crowd spanning 100+ countries and 40+ languages, we provide skilled annotators who have diverse backgrounds with expertise in a wide range of fields.
You can also select annotators for your project by country, language, skill, and expertise.
Get started with LLM training with Toloka here.