5minAI Context Window - How does it affect AI application?
Artificial intelligence (AI) is a broad term that includes many fields and applications, such as natural language processing, computer vision, speech recognition, machine learning, and more. AI systems often rely on large amounts of data to learn from and perform various tasks, such as understanding, generating, or translating natural language, recognizing faces or objects in images, or playing games.
However, data is not always available in a convenient or structured form. Sometimes, data is presented as a long or complex sequence of information, such as a text document, a conversation, or a video. In such cases, AI systems need to process the data in smaller chunks or segments, rather than as a whole. This is where the concept of context window comes in.
What is the Context Window?
A context window is the maximum amount of data or information that an AI system can process or handle at a given time. It can be thought of as the working memory or the attention span of the system. For example, when you read a long article, you cannot remember or comprehend every single word or sentence in it. Instead, you focus on a smaller portion of the text that is relevant or interesting to you at the moment. Similarly, when an AI system processes a long or complex sequence of data, it cannot attend to or use every single element or feature in it. Instead, it focuses on a smaller segment of the data that is relevant or useful for the task at hand.
The size or length of the context window depends on various factors, such as the type of data, the type of task, the type of system, and the available resources. For example, the context window for natural language processing (NLP) systems, which deal with text or speech data, is usually measured in terms of tokens, words, or characters. It may be limited by the available resources, such as memory, processing power, or time. As for computer vision systems, which deal with image or video data, is usually measured in terms of pixels, regions, or frames.
What is the Context Window for GPTs and Llama2?
GPTs and Llama2 are two examples of large language models (LLMs) that can understand and generate natural language or code. LLMs are a type of neural network architecture that use a mechanism called self-attention to learn from and process large amounts of text data. Self-attention allows the system to look at a piece of text and figure out which parts are important, relevant, or related to each other. For example, when the system sees the word “apple”, it can use self-attention to find other words that are related to “apple”, such as “fruit”, “red”, or “pie”. However, self-attention is not unlimited. It can only look at a certain number of tokens or words at a time. This is the context window for LLMs. The context window for LLMs is usually determined by the size or capacity of the model, which is measured in terms of parameters. Parameters are the numerical values that the model learns from the data and uses to perform the tasks. The more parameters a model has, the more data it can learn from and process, and the larger its context window can be.
GPTs and Llama2 are two of the largest and most advanced LLMs available today. They have billions of parameters and can process thousands of tokens or words at a time. However, they also have different context windows depending on the variant or version of the model. For example, GPT-4 Turbo has a context window of 128,000 tokens, which is roughly 100,000 words. This means that GPT-4 Turbo can process and respond to a text input that is as long as a novel. Llama2 has a context window of 4,096 tokens, which is roughly 3,000 words. This means that Llama2 can process and respond to a text input that is as long as a short story. However, Llama2 can also use techniques such as interpolation or parallel context windows to extend its context window to up to 32,000 tokens, which is roughly 24,000 words. This means that Llama2 can process and respond to a text input that is as long as a chapter of a book.
How Does the Context Window Affect User-AI Interaction?
The context window is an important factor that affects the quality and efficiency of user-AI interaction. User-AI interaction refers to the communication or exchange of information between a human user and an AI system, such as a chatbot, a virtual assistant, or a recommender system. The context window determines how much information the AI system can process and use from the user input, and how much information the AI system can provide or generate for the user output.
A larger context window can improve the user-AI interaction in several ways. It can increase the accuracy or correctness of the AI system. A larger context window allows the AI system to consider more information from the user input and use it to perform the task or provide the answer. For example, if the user asks a question that requires multiple pieces of information to answer, such as “What is the capital of the country that borders France and Germany?”, a larger context window can help the AI system to identify and use all the relevant information, such as “country”, “France”, “Germany”, and “border”, and provide the correct answer, which is “Luxembourg”. A smaller context window may cause the AI system to miss or ignore some of the information and provide an incorrect or incomplete answer, such as “Paris” or “Berlin”.
A larger context window allows the AI system to maintain or remember more information from the previous or ongoing user input and use it to generate the current or future user output. For example, if the user has a conversation with the AI system that involves multiple topics or turns, such as “How are you today?”, “What is your favorite movie?”, and “Who is the director of that movie?”, a larger context window can help the AI system to keep track of or refer to the previous topics or turns and generate coherent or consistent responses, such as “I am fine, thank you.”, “My favorite movie is The Matrix.”, and “The director of that movie is Lana Wachowski.”.
Small context window may cause the AI system to forget or lose track of the previous topics or turns and generate incoherent or inconsistent responses, such as “I am fine, thank you.”, “My favorite movie is The Matrix.”, and “The director of that movie is Steven Spielberg.”.
Large context window helps to increase the richness or diversity of the AI system. A larger context window allows the AI system to access or generate more information from the user input and provide or create more information for the user output. For example, if the user asks for a recommendation or a suggestion from the AI system, such as “What should I watch on Netflix tonight?”, a larger context window can help the AI system to consider or generate more options or alternatives from the user input and provide or create more options or alternatives for the user output. For example, the AI system can use the user’s preferences, history, or ratings to recommend or suggest different genres, titles, or categories of movies or shows, such as “You might like sci-fi movies, such as The Matrix, Inception, or Interstellar.”, “You might like comedy shows, such as The Office, Friends, or The Big Bang Theory.”, or “You might like documentaries, such as Planet Earth, The Social Dilemma, or My Octopus Teacher.”. A smaller context window may limit the AI system to consider or generate fewer options or alternatives from the user input and provide or create fewer options or alternatives for the user output. For example, the AI system may only recommend or suggest one genre, title, or category of movies or shows, such as “You might like The Matrix.”.
How Does the Knowledge Base Affect the Context Window?
A knowledge base is a collection of facts, information, or data that an AI system can use to perform various tasks, such as answering questions, generating content, or making decisions. A knowledge base can be internal or external to the AI system. An internal knowledge base is the information that the AI system learns from the data or the training process, such as the parameters, the weights, or the embeddings of the model. An external knowledge base is the information that the AI system accesses from other sources, such as databases, ontologies, or web pages.
A knowledge base can affect the context window in several ways. First, it can increase the accuracy of the response inside context window. A knowledge base can provide additional information that the AI system can use to process or handle more data or information at a given time. For example, if the AI system uses an external knowledge base, such as Wikipedia, to answer a question from the user, such as “Who is the president of France?”, it can use the information from the Wikipedia page of France to extend its context window and provide a more detailed or accurate answer, such as “The president of France is Emmanuel Macron, who was elected in 2017 and is the youngest president in the history of France.”.
Second, it can improve the quality or efficiency of the context window. A knowledge base can provide relevant or useful information that the AI system can use to process or handle the data or information more effectively or efficiently. For example, if the AI system uses an internal knowledge base, such as a pre-trained language model, to generate content for the user, such as a poem, a story, or a code, it can use the information from the pre-trained language model to improve its context window and generate more coherent, consistent, or diverse content.
Conclusion
The context window is the maximum amount of data or information that an AI system can process or handle at a given time. It depends on various factors, such as the type of data, the type of task, the type of system, and the available resources. The context window can be improved by using a knowledge base, which is a collection of facts, information, or data that an AI system can use to perform various tasks, which will affect the quality and efficiency of user-AI interaction. A larger or better context window can increase the accuracy, coherence, or richness of the AI system, and thus improve the user satisfaction, engagement, or trust.
