Definitions

A centralized glossary of terms and their meaning, to clarify the language used throughout the top 10.

• Confabulation - A term originating in psychiatry, where it is used to refer to a patient's tendency to create false narratives either with the intent to deceive, or because they actually believe what they are saying is true. This definition closely aligns to what LLMs do when they generate output that is not based on real-world input or information. An LLM may confabulate output for a variety of reasons. When they do, the resulting output may be false, nonsensical, offensive, dangerous, or contain references to things that do not exist. Often the LLM will present such information confidently despite the fact that it is incorrect. Using the term confabulation to refer to this behavior is preferred to the term hallucinate among AI scientists and researchers, as it avoids anthropomorphizing the underlying technology.
• Embeddings - the numerical representation of words, phrases, or paragraphs of a text as a vector of real numbers. Embeddings capture the semantic meanings and relationships between words, resulting in similar vector representations for words that are semantically related.
• Fine-tuning - The process of further refining a Foundation Model to improve the performance of the model on a specific task. Common methods of fine tuning include:
  • Training on task-specific datasets
  • Embeddings
  • Engineering task-specific system prompts
• Foundation Model - A large language model that is trained on a broad set of diverese data to operate across a wide range of use cases.
• Hallucinate - In the context of LLMs, the term hallucinate can refer to the process of generated content (usually text or code) that is not based on any real-world artifacts. Describing this behavior using the term confabulation is becoming preferred preferred among AI scientists and researchers, as it avoids anthropomorphizing the underlying technology.
• Inference - The process of using a trained model to generate predictions or responses, usually as an API or web service.
• LLM - Large language model. A type of artificial intelligence (AI) that is trained on a massive dataset of text and code. LLMs used natural language processing to process requests and generate data.
• LLM Agent - A piece of code that formulates prompts to an LLM and parses the output in order to perform an action or a series of actions (typically by calling one or more plugins/tools).
• LLM Agent Memory - A piece of code and dedicated storage that allows LLM Agent to remember interactions in the short and long term. (e.g., storing chat history between user sessions). LLM Agent Memory does not necessarily store the entire conversion it can store a summary, n last messages, etc.
• LLM Plugin - A piece of code that exposes external functionality to an LLM Agent; e.g., reading a file, fetching the contest of a URL, querying a database, etc.
• NLP (Natural Language Processing) - The branch of computer science focused on teaching computers to speak.
• Self-supervised learning - A type of machine learning in which the model is trained to learn from unlabeled data. In the case of LLMs, self-supervised learning is often used to train the model to predict the next word in a sequence.
• Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.
• Transformer - A type of neural network architecture that is commonly used to train LLMs. Transformers are able to learn long-range dependencies between words, which makes them well-suited for natural language processing tasks.
• Transfer learning - A process of using a model that has been trained on one task to improve the performance of a model on a different task. Transfer learning is often used to save time and resources when training new models.
• Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsupervised”).