Learn Before
Maximum a Posteriori (MAP) Decoding
Maximum a Posteriori (MAP) decoding is a search objective in text generation that aims to identify the single output sequence y which has the highest conditional probability Pr(y|x) given an input x. This approach focuses on finding the single most likely hypothesis from the entire space of possible outputs.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Sampling-Based Search for LLM Inference
Sequence Evaluation using Log-Probability
Deterministic Decoding Algorithms
Modifying the Search Objective to Improve Decoding
Maximum a Posteriori (MAP) Decoding
Speculative Decoding
Structured Search in Decoding
Trade-off between Search Quality and Computational Efficiency in Heuristic Search
An engineer is building a real-time chatbot that must respond to user queries very quickly. To achieve this speed, the engineer implements a text generation strategy that, at each step of forming a response, considers only a small subset of the most likely next words instead of all possible words in the vocabulary. What is the fundamental trade-off inherent in this design choice?
Evaluating a Decoding Algorithm Claim
Analysis of Competing Text Generation Systems
Learn After
A language model is tasked with generating a 50-word summary. The stated goal is to find the single sequence of 50 words that has the highest overall probability given the input text. Why is it computationally infeasible to guarantee finding this exact sequence in practice?
Selecting a Text Generation Objective
Local vs. Global Optimality in Text Generation