LLM Prediction with Full Context
The prediction of a Large Language Model, denoted as , when provided with a full context and an input , is determined by selecting the output that maximizes the conditional probability. This process is formally expressed by the formula: This prediction often serves as the target or 'gold standard' when learning a compressed representation of the context.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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LLM Prediction with Full Context
LLM Prediction with Compressed Context
Mathematical Formulation of Prompt Ensembling
Formula for Scoring Reasoning Paths by Counting Correct Steps
A classification model is given an input,
x, and must choose an output,y, from the set of possible classes {A, B, C, D}. The model's decision rule is to select the class that has the highest conditional probability,Pr(y|x). Given the following probabilities calculated by the model for the inputx, what will its final prediction be?Pr(y=A | x)= 0.15Pr(y=B | x)= 0.55Pr(y=C | x)= 0.25Pr(y=D | x)= 0.05
Model Prediction vs. Ground Truth
Analyzing a Model's Prediction Choice
Learn After
Formula for Optimizing Soft Prompts via Context Compression
Formula for Soft Prompt Optimization via Log-Likelihood Maximization
Formula for Soft Prompt Optimization by Minimizing KL Divergence
An inference engine using a continuous batching strategy is currently processing a set of text generation requests that fully utilizes its processing capacity. At this point, a new, additional request arrives. What is the most likely immediate action the system's scheduler will take regarding this new request?
A language model is provided with a context
c('Translate the following sentence for a medical professional') and an inputz('Le patient présente une pyrexie'). The model computes the conditional probabilities for several potential English translations (y). Based on the principle of selecting the output that maximizes the conditional probability given the full context and input, which translation should the model choose as its prediction?Analyzing Contextual Influence on LLM Predictions