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Prompting as a Conditional Probability Task
The process of generating text with a Large Language Model is formally framed as a conditional probability task. Given an input prompt, denoted as , the model's objective is to produce an output text, , that maximizes the conditional probability . The prompt, therefore, functions as the condition upon which the prediction is based.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Prompt Tuning
The Power of Scale for Parameter-Efficient Prompt Tuning
Basic Workflow of Prompt
Prompt Decomposition
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
Example of a Complete Prompt for Machine Translation
Importance of Prompting for Response Quality
Prompting as a Conditional Probability Task
Constraining LLM Predictions to a Predefined Label Set
Prompt Ensembling
Structural Components of a Simple Prompt
Input Embeddings in LLMs
Input Token Sequence in Language Models
Varied Usage of the Term 'Prompt' in Literature
Definition of Prompting
A user provides the following text to a language model: 'Summarize the key points of the following article in three bullet points. Article: [Text of a long article follows here...]'. The model then generates a three-point summary. Based on the formal definition of how these models process information, which of the following best describes the 'prompt' in this interaction?
Analyzing the Components of a Model Input
Classification via Cloze Task Reframing
A language model is given the input text, 'Translate the following sentence to French: The cat is on the mat.' The model's objective is to generate the most likely sequence of words that completes this task. According to the formal, probabilistic definition of how these models operate, what is the fundamental role of the input text?
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A user wants a language model to generate a specific two-sentence summary about the sun. Consider two possible inputs:
Input A:
Summarize the sun.Input B:In two sentences, describe the sun's composition and its role as the center of our solar system.From the perspective of text generation as a task of maximizing the probability of an output given an input, which statement best analyzes the relationship between these inputs and the desired output?
Explaining Model Behavior with Conditional Probability
Analyzing Prompt Specificity through Conditional Probability