Hypothesis in LLM Inference

Mathematical Formulation of the Search Problem in LLM Inference

Exploration vs. Exploitation in LLM Search

Search Tree Structure in Token Generation

Heuristic Search Algorithms for LLM Inference

Efficient Generation of Candidate Solutions via Search Algorithms

Search for Optimal or Sub-optimal Sequences in LLM Inference

Root of the Search Space as a Representation of Input (x)

A text generation model has a vocabulary of 10,000 possible words it can choose from for each position in a sequence. If this model were to find the optimal output by evaluating every single possible sequence, how would the total number of sequences to check change if the desired output length is increased from 3 words to 5 words?

Evaluating an Inference Strategy

The Impracticality of Exhaustive Search

Historical Context and Computational Challenges of Maximum Probability Prediction

Mathematical Representation of an Output Sequence

Model-Specific Optimizations for LLM Inference

Modeling and Efficient Computation of Conditional Token Probabilities

Efficient Generation of Candidate Solutions via Search Algorithms

An AI research team is developing a new generative model for creating complex musical compositions. They find that while their model can accurately calculate the probability of any given short musical phrase, generating a full, high-quality, multi-minute symphony is computationally intractable because they cannot feasibly check every possible combination of notes to find the absolute best one. How does this team's challenge relate to the broader field of artificial intelligence?

Comparing Computational Challenges in AI Tasks

Identifying Common Computational Structures in AI

Accuracy-Efficiency Trade-off in LLM Inference