Basics of LLMs
This term refers to a type of artificial intelligence model trained on vast amounts of text to predict and generate human-like language.
Large Language Model (LLM)
LLMs are frequently used for this task, where they automatically generate paragraphs or documents.
Text Generation
This measure refers to the number of trainable parameters in a model, often running into billions in LLMs.
Parameters
This term describes the unintentional copying of biased language or ideas from the training data into LLM outputs.
Bias
This technology, built on LLMs, is used by sales teams to automate responses and provide data-driven suggestions during customer interactions.
This technique helps LLMs process words in context, focusing on word relationships across a sequence.
Attention
A common use of LLMs is to summarize long articles into shorter versions, referred to as this.
Summarization
LLMs rely heavily on this type of dataset, made up of large amounts of text from books, websites, and other sources.
Corpus
One limitation of LLMs is that they sometimes create convincing but false or misleading information, known as this.
Hallucination
LLMs are widely used to analyze this type of data, crucial for customer sentiment analysis.
Unstructured Data
LLMs like GPT are built on this type of neural network architecture known for sequential data processing.
Transformer
LLMs can engage in this type of interactive communication with users, often seen in customer support bots.
Conversation AI or Chatbot
During training, LLMs use this algorithm to adjust the model’s weights based on errors made in prediction.
Backpropagation
LLMs are trained on publicly available data, which sometimes includes private or copyrighted material, raising concerns about this.
LLMs can assist HR teams by screening these documents during the hiring process.
Resumes
LLMs are often trained using this unsupervised learning task, where they predict missing words in sentences.
Masked Language Modeling
This task involves LLMs understanding human requests and retrieving relevant information, often using search engines.
Question Answering
LLMs need this type of high-performance hardware to process vast datasets and train their billions of parameters.
GPUs (Graphics Processing Units)
Companies use this practice to ensure LLMs don't produce harmful or offensive content, using rule-based systems or human moderators.
Content Moderation or Filtering
This type of analytics involves LLMs analyzing customer reviews, emails, and social media to understand customer behavior.
Sentiment Analysis
The process of improving an LLM after training by tuning it on specific tasks with human feedback is called this.
Fine-tuning
LLMs are employed in software development to assist developers in writing code. This application is known as what?
Code Generation
This method is used to avoid overfitting in LLMs by randomly “dropping” some connections in the network during training.
Dropout
LLMs might reflect harmful stereotypes due to this concept, where training data reflects existing societal inequalities.
Algorithmic Bias or Data Bias
Name one of the main challenges that arise from deploying LLMs in real-world business environments.
Bias, High Computational Cost, and Data Privacy Concerns