Beam search
Beam search is a heuristic search and decoding technique that iteratively expands a limited set of the highest-scoring partial candidates.
Natural Language Processing in Action, Second Edition
Beam search generates sequences by retaining a limited set of high-scoring partial paths and expanding them over multiple decoding steps.
- How it is used
- Guides generative language-model decoding toward high-scoring sequences without exploring every possible path.
- How deep it goes
- Explains retained paths, cumulative probability, breadth, depth, and recursive expansion, but does not provide beam-search implementation code.
- Examples
- Uses a road-trip analogy and a decision-tree figure showing two possible beams.
- What you can do after
- Apply beam search to bound generative decoding while retaining multiple candidate token sequences.
Read how Natural Language Processing in Action, Second Edition explains beam search →
Transformers in Action
Beam search is a decoding algorithm that maintains a fixed number of highest-probability partial token sequences and expands each sequence at every step.
- How it is used
- Guides text generation by retaining several likely token sequences rather than committing to each greedy choice.
- How deep it goes
- Explains expansion and pruning through breadth-first-search intuition and a generation call, without specifying scoring refinements.
- Examples
- Illustrates two beams extending “LLMs” and configures five beams to return three sequences.
- What you can do after
- Apply it via `model.generate` using `num_beams`, `num_return_sequences`, `early_stopping`, and `do_sample=False`.
Optimization Algorithms: AI techniques for design, planning, and control problems
An informed search technique that retains only a limited number of the most promising partial solutions at each step.
- How it is used
- Finds short paths in graphs or trees when limiting memory and search breadth matters.
- How deep it goes
- Explains state retention, expansion, ranking, complexity, thresholds, and failure modes, stopping short of optimality guarantees.
- Examples
- Demonstrates S-to-G routing with w = 2 and larger routing experiments using k = 10 and n = 20.
- What you can do after
- Apply it via Python graph code by tuning beam width, generated children, costs, and route-expansion parameters.
Deep Learning with Python, Third Edition
A decoding method that keeps a fixed number of candidate generation paths and expands them step by step.
- How it is used
- Uses multiple candidate token chains to guide text-generation decoding.
- How deep it goes
- Defines fixed-width, stepwise exploration but stops short of implementation details or a worked beam-search example.
- What you can do after
- Evaluate beam search as a decoding alternative to single-path and sampling-based generation.
Read how Deep Learning with Python, Third Edition explains beam search →
Sutskever's List
Beam search is a sequence-decoding algorithm that retains a fixed-size set of high-scoring partial candidates at each expansion step.
- How it is used
- Turns probabilistic speech-model scores into a transcript while avoiding exhaustive sequence search.
- How deep it goes
- Explains candidate expansion, acoustic and language-model scoring, and pruning, without giving formal scoring equations.
- Examples
- Uses Deep Speech 2’s English and Mandarin beam sizes and Mandarin’s shared-sound search challenge.
- What you can do after
- Understand how retaining and pruning alternatives trades decoding cost against transcript quality.
Related concepts
- greedy algorithmalternative to · A greedy algorithm builds a solution incrementally by choosing the locally best available option at each step, without generally accounting for all future consequences or revising prior choices.
- informed searchkind of · Informed search is a search strategy that uses problem-specific knowledge or heuristic estimates to prioritize promising states or paths.
- decoding strategykind of · A decoding strategy is a procedure for selecting successive tokens from a model’s output distribution to generate an output sequence.
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