LLMs, or Large Language Models, have become a hot topic of discussion lately. They are widely mentioned in conversations about ChatGPT and other related technologies. What exactly are LLMs, how do they function, and what are their practical applications? In this guide, we will try to answer these questions.
Introduction to LLMs
What are LLMs, how does ChatGPT and other LLMs work?
LLM stands for “Large Language Model.” These are advanced AI systems that have been trained on a lot of text data to understand and generate human-like text. They can do many language-related tasks like answering questions, having conversations, summarizing text, translating languages, and more. They are designed to mimic human language abilities and provide helpful and accurate responses.
OpenAI has played a significant role in this field over the past few years, contributing with their models and research. However, there are other companies and organizations involved as well. For example, Meta has introduced their OPT, OPT-IML, and LLaMA models. Google has released FLAN-T5 and BERT. Stability AI has developed StableLM, and Stanford has Alpaca. Additionally, there are many other open-source models available. These various players contribute to the advancements in language models and offer different approaches and capabilities in this space.
Training an LLM Model
On a high level, training an LLM model typically involves three steps:
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Data Collection: The first step is to gather the data that will be used to train the model. This data can be collected from various sources such as Wikipedia, news articles, books, websites, and more.
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Training: Once the data is collected, it goes through a training pipeline. During this process, the data is cleaned and preprocessed to ensure it is in a suitable format for training. The training itself can be a time-consuming task that requires significant computational power.
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Evaluation: After the model has been trained, the final step is to evaluate its performance. This involves testing the model on various tasks such as question answering, summarization, translation, and more. By assessing how well the model performs on these tasks, its effectiveness and capabilities can be determined.
The output of the training pipeline is an LLM model, which consists of parameters or weights that capture the knowledge acquired during the training process. These parameters or weights are typically serialized and stored in a file. This file can then be loaded into any application that requires language processing capabilities, such as text generation, question answering, language processing, and more.
Types of LLMs
LLMs can be categorized into two types:
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Base LLMs: These LLMs are designed to predict the next word based on the training data. They are not specifically designed to answer questions or carry out conversations. For example, if you input the sentence: “In this book about LLMs, we will discuss,” a base LLM might complete the sentence with relevant text but not directly answer the question.
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Instruction Tuned LLMs: These LLMs are designed to follow given instructions using the data they have been trained on. Instead of simply autocompleting text, they aim to answer questions or provide relevant responses based on the instructions. For example, if you input the question “What are LLMs?” into an instruction tuned LLM, it will use its training data to try to answer the question directly.
Instruction Tuned LLMs are built on top of Base LLMs by further training them using a large dataset covering sample instructions and the expected model performance based on those instructions. The model is then fine-tuned using techniques like Reinforcement Learning with Human Feedback (RLHF) to improve its performance over time.
Conclusion
LLMs are indeed a powerful tool that can be utilized to address a wide range of language-related tasks. They have found applications in various industries such as healthcare, finance, education, and more. By automating processes and enhancing efficiency, LLMs have the potential to revolutionize our interactions with computers and simplify our lives. They offer promising opportunities for streamlining tasks and improving overall productivity.