Large Language Models (LLMs) have revolutionized natural language processing, enabling practical applications that address real-world challenges. This article delves into the concepts and functionality of LLMs, exploring their potential and limitations.
LLMs are powerful pre-trained models that use sophisticated probability theory to generate long, sensible text one word at a time. Their size is measured by the number of parameters, weights, and biases, as well as the massive amount of data they are trained on – billions of words, tokens, and millions of documents.
Despite their capabilities, LLMs have limitations. For instance, a model may struggle to diagnose medical domain cases or predict legal outcomes accurately. This is because it only knows what it has been trained on and may not have seen every possible scenario.
- Out-of-scope responses: LLMs may provide answers that are not relevant or accurate in certain contexts.
- Inappropriate responses: Toxic or harmful content can emerge from LLMs if they are not properly trained or monitored.
- Hallucinations: Models may start generating inaccurate or false information, as seen in the example of a conversation with ChatGPT that began to fabricate details.
Entity recognition, text extraction, and information extraction can be performed using LLMs. This is because they know languages, enabling them to perform these tasks. Given an unstructured document, LLMs can collect information and put it into a tabular format, such as converting textual documents into structured formats.
When aiming for generality, training the model on a vast amount of data becomes crucial. This process requires significant effort, especially when tagging data for specific objectives. On the other hand, if the goal is to train a model for a specific task, the required data and tagging efforts are significantly reduced.
Knowledge graphs play a vital role in analyzing and discovering relationships. They have numerous applications, including drug discovery, where they aid in accelerating the process by analyzing previous knowledge and making predictions about new information.
Discovery is indeed possible with LLMs, but there are limitations to their capabilities. A certain amount of domain knowledge is required to place boundaries around what a model can do. This is why it’s essential to define rules for LLMs, ensuring they don’t exceed their limits and generate inaccurate or harmful content.
LLMs can be trained for specific domains, making them useful as co-pilots for writing codes or answering text-based questions. However, there are absolute bounding boxes that we don’t want models to break, such as the example of a conversation with ChatGPT that began to fabricate details. By placing rules and guidelines around LLMs’ capabilities, we can ensure they operate within their limitations.
Exploratory analysis is crucial in discovering risks associated with using LLMs. This process helps us identify potential issues before deploying models in production environments. Additionally, there are certain risks that will only be discovered during production, making it essential to have a thorough understanding of an LLM’s capabilities and limitations.
