Capabilities of LLMs
Uncover the capabilities of LLMs.
Overview
LLMs have emerged as transformative tools in natural language processing, showcasing an array of capabilities that elevate their utility across various applications. With their advanced language understanding and generation abilities, LLMs, such as GPT-3 and BERT, can seamlessly navigate tasks ranging from language modeling and question answering to translation, arithmetic, and news article generation. These models demonstrate an unparalleled capacity to comprehend contextual nuances, generate coherent and contextually relevant text, and adapt to diverse language tasks.
Their versatility is further exemplified by their ability to tackle novel challenges and adapt to specialized domains through fine-tuning. LLMs are powerful engines for automating language-related tasks, breaking language barriers, and pushing the boundaries of what is achievable in natural language processing. As they continue to evolve, LLMs are poised to play a pivotal role in shaping the future of human-machine interactions, information retrieval, and content generation in artificial intelligence.
Tasks performed by LLMs
Let’s explore the capabilities of LLMs.
Language modeling
Language modeling is a fundamental task in natural language processing where the model is trained to predict the next word in a sequence of words given the context of the preceding words. The goal is to capture the probability distribution of words in a language and learn the relationships between them.
Perplexity
Perplexity measures how well a probability distribution or probability model predicts a sample. Lower perplexity values indicate better predictive performance of the LM on the given dataset.
The formula for this metric is as follows:
Where:
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