What is an LLM
An LLM, or large language model, is a type of artificial intelligence system that has been trained on vast amounts of text data to understand and generate human-like language. These models are capable of performing a wide range of natural language processing tasks, from answering questions and translating languages to writing creative fiction and even generating code.
How do large language models work?
A key factor in how LLMs work is the way they represent words. Earlier forms of machine learning used a numerical table to represent each word. But, this form of representation could not recognize relationships between words such as words with similar meanings. This limitation was overcome by using multi-dimensional vectors, commonly referred to as word embeddings, to represent words so that words with similar contextual meanings or other relationships are close to each other in the vector space.
Using word embeddings, transformers can pre-process text as numerical representations through the encoder and understand the context of words and phrases with similar meanings as well as other relationships between words such as parts of speech. It is then possible for LLMs to apply this knowledge of the language through the decoder to produce a unique output.
One of the most well-known examples of an LLM is ChatGPT (Generative Pre-trained Transformer), developed by OpenAI. This model was trained on an incredible 175 billion parameters, making it one of the largest and most sophisticated language models ever created. ChatGPT can engage in coherent conversations, write articles and stories, and even solve programming problems.
It’s important to understand that today’s development in the field of LLMs are made possible because of an innovation known as transformers. The transformer enables a specific type of neural network architecture that has proven highly effective for natural language processing tasks.
The transformer was first introduced in a 2017 paper titled "Attention Is All You Need" by researchers at Google Brain. It revolutionized the field of NLP by eschewing the sequential processing of previous models like recurrent neural networks (RNNs) in favor of a more parallelized approach using an "attention mechanism.”
Here's a high-level overview of how transformers work in LLMs:
Embedding: The input text is first converted into numeric vector representations called embeddings.
Encoder: The embeddings pass through an encoder module made up of multiple transformer encoder layers. Each layer applies self-attention, allowing the model to weigh different word representations against each other when encoding meaning.
Decoder: The output embeddings from the encoder are then processed by a transformer decoder module, also applying self-attention along with encoding-decoding attention over the encoder representations.
Output: Finally, the decoder produces an output probability distribution over the vocabulary for predicting the next word token.
How are large language models trained?
Transformer-based neural networks are very large. These networks contain multiple nodes and layers. Each node in a layer has connections to all nodes in the subsequent layer, each of which has a weight and a bias. Weights and biases along with embeddings are known as model parameters. Large transformer-based neural networks can have billions and billions of parameters. The size of the model is generally determined by an empirical relationship between the model size, the number of parameters, and the size of the training data.
Training is performed using a large corpus of high-quality data. During training, the model iteratively adjusts parameter values until the model correctly predicts the next token from the previous sequence of input tokens. It does this through self-learning techniques which teach the model to adjust parameters to maximize the likelihood of the next tokens in the training examples.
4 Key advantages of LLMs:
Natural Language Understanding: LLMs have a deep grasp of human language, allowing them to comprehend context, nuance, and intent.
Versatility: These models can be adapted for a wide range of tasks, from language translation to content generation and analysis.
Scalability: LLMs can be fine-tuned on specific datasets, enabling them to acquire specialized knowledge in various domains.
Efficiency: Once trained, LLMs can generate human-like text quickly and at a fraction of the cost of human writers.
4 Limitations of LLMs:
Bias and Inconsistency: LLMs can inherit biases present in their training data, leading to potentially harmful or inconsistent outputs.
Lack of Common Sense: Despite their language capabilities, LLMs can struggle with tasks that require common sense reasoning or real-world knowledge.
Factual Errors: LLMs can generate plausible-sounding but factually incorrect information, especially on topics outside their training data.
Ethical Concerns: The potential misuse of LLMs for generating misinformation, hate speech, or malicious content raises ethical concerns.
Large language models are a remarkable achievement in the field of natural language processing, but they also come with their own set of challenges. As these models continue to evolve, it's crucial to address their limitations and ensure they are developed and deployed responsibly, with safeguards in place to mitigate potential risks.
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