A language model (LM) is a type of machine learning model trained over a corpus of textual data (books, news articles, wikipedia, other online web content) to assign a probability distribution for words. In simpler terms, the model attempts to predict the next word given a sequence of words. The primary goal of a language model (LM) is to learn the patterns, structures, and relationships within a given text and predict the words or phrases that are likely to come next in a sequence of text. This type of understanding by a machine learning model has a wide range of applications in various language-related tasks (or Natural Language Processing tasks) such as language translation, question answering systems, search engines, text generation and topic modeling.
Evolution of Language Models
The evolution of large language models has been a remarkable journey in the field of artificial intelligence and natural language processing. Language models have progressed from simple statistical methods to powerful deep learning architectures, revolutionizing the way computers understand and generate human language. Here’s an overview of the evolution of language models:
- Rule Based Approaches: Early language processing systems relied on handcrafted rules and grammatical structures to analyze and generate text. These rule-based systems were limited in handling the nuances and complexities of natural language.
- Statistical Language models (N-gram Models): Statistical language models introduced probabilistic techniques to language processing. N-gram models, for instance, predicted the probability of a word given its previous n-1 words. While they improved language understanding to some extent, they lacked the ability to capture long-range dependencies.
- Hidden Markov Models (HMMs): HMMs combined statistical modeling with grammatical rules to analyze sequences of words.
- Neural Language Models: In the late 1990s, the resurgence of neural networks led to the development of early language models that used simple neural architectures like Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Gated Recurrent Units (GRUs) for NLP tasks. While these models showed promise, they struggled with capturing long-range dependencies and context. Long Short-Term Memory (LSTM) networks were an improvement over traditional RNNs as they worked better in retaining long-range dependencies in sequential data and mitigating vanishing gradient issues. However, LSTM models struggled with high computational power requirements and parallelization
- Word embeddings: Around 2013, word embeddings like Word2Vec, GloVe, and FastText were introduced. These methods represented words as dense vectors in a continuous vector space, capturing semantic relationships between words and improving the performance of various NLP tasks.
- Transformer models: The introduction of the Transformer architecture in the paper “Attention is All You Need” in 2017 marked a significant turning point. Transformers used self-attention mechanisms to process input data in parallel, enabling the modeling of long-range dependencies. This architecture paved the way for major advancements in language models.
- Pre-trained large language models: A slew of large language models followed leveraging the transformer architecture introduced in 2017. In 2018, Google introduced Bert (used in Google search engines) and in 2019, OpenAI released Generative Pre-trained Transformer 2 (GPT-2), a large-scale, unsupervised model pretrained on a massive amount of text data. GPT-2 gained attention for its impressive language generation capabilities. In November 2022, ChatGPT, a question-answering system leveraging the GPT-3 architecture was released by OpenAI and it took the world by storm drawing massive attention in the space of NLP, Generative AI and large language models (LLMs). Several other large language models (LLMs) followed, including PaLM, T5, LaMDA, DALL-E, LLaMa, and others.
Infographic depicting timeline of language models
Presented below is the Infographics of the evolution timeline of the language models:
Advancements are rapidly unfolding in the realms of text, speech recognition, and vision, driven by the utilization of deep neural network architecture.
- This is an excellent video by Code Emporium that explores the evolution of language models while discussing their context and applications in solving real-world Natural Language Processing tasks. (Runtime: 16 mins)
- An introduction to Large Language Models (LLMs) by Google discusses how LLMs work, various use cases, and how you can interact with them using prompts. (Runtime: 6 mins)