Llms

Large Language Models (LLMs) are neural network-based systems trained on vast amounts of text data to generate human-like responses and perform various language tasks. These models learn statistical patterns from their training data and use these patterns to predict and generate sequences of text. LLMs have demonstrated capabilities across translation, summarization, question-answering, and writing tasks.

A significant challenge associated with LLMs is the phenomenon of hallucination, where the model generates information that is factually incorrect or nonsensical. This occurs because the models prioritize linguistic plausibility over factual accuracy, often fabricating details that sound convincing but are unsupported by the underlying data.

To mitigate these issues, developers employ strategies such as prompt engineering and Retrieval-Augmented Generation (RAG). Prompt engineering involves structuring input queries to guide the model toward more accurate outputs, while RAG integrates external knowledge bases to provide the model with verified context during generation, thereby reducing the likelihood of factual errors.