Large Language Model (LLM)

A large language model (LLM) is a type of artificial intelligence model that is designed to understand and generate human-like language on a large scale. LLMs are typically trained on massive amounts of text data from a wide range of sources, such as books, websites, articles, and other textual resources. These models utilize deep learning techniques, particularly using architectures like transformers, to capture complex patterns and dependencies in language. Read the article: What are LLMs, and how are they used in generative AI?

LLMs are trained to process and generate coherent and contextually relevant responses based on the input they receive. They can understand and generate text in multiple languages and can perform a variety of language-related tasks, including language translation, text summarization, question answering, text completion, and more.

One of the most well-known and influential LLMs is OpenAI’s ChatGPT or just GPT (Generative Pre-trained Transformer) series, such as GPT-4. These models have achieved capabilities in generating human-like text and have been used in various applications, including chatbots, virtual assistants, content generation, and creative writing.

The training process for LLMs involves exposing the model to a large corpus of text data and using techniques like unsupervised learning to learn the statistical patterns and relationships within the language. The models are trained to predict the next word or sequence of words based on the context provided by the preceding words. This process enables the models to capture syntactic, semantic, and contextual nuances of language.

LLMs limitations and challenges

LLMs can sometimes generate incorrect or nonsensical responses, struggle with understanding nuances, and may be sensitive to biases present in the training data. Additionally, LLMs require significant computational resources for training and inference, making them computationally expensive. Despite these limitations, large language models have demonstrated tremendous potential in advancing natural language processing capabilities and enabling human-like interactions with AI systems. Ongoing research and development efforts continue to push the boundaries of LLMs, aiming to improve their accuracy, interpretability, and ethical use.

Data Management and AI

In May 2023, Krishna Subramanian, cofounder and COO of Komprise wrote an article for Datanami entitled: Data Management Implications for Generative AI. She summarized 3 areas that need more attention:

  1. Data governance and transparency with training data
  2. Data segregation and data domains
  3. The derivate works of AI

Her conclusion:

Enterprises should tread carefully and ensure they clearly understand the data exposure, data leakage and potential data security risks before using AI applications.


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