Inference · intermediate
Beam Search
Beam search explores several candidate continuations in parallel, keeping the top-k partial sequences at each step. Common in translation; rare in modern LLM chat.
Explanation
At each step beam search expands every current candidate by every possible next token, scores all resulting sequences by cumulative log-probability, and keeps the top beam_width of them. Output is the highest-scoring complete sequence.
Strong for sequence-to-sequence tasks with a clear correct answer (machine translation, summarization eval). Rarely used in open-ended chat — it tends to produce safe, generic completions and is much more expensive than sampling.
Examples
- Translation systems with beam width 4-10.
- Decoding for ASR (speech-to-text).
Frequently asked
What is Beam Search?
Beam search explores several candidate continuations in parallel, keeping the top-k partial sequences at each step. Common in translation; rare in modern LLM chat.
What is an example of beam search?
Translation systems with beam width 4-10.
How is Beam Search related to Greedy Decoding?
Beam Search and Greedy Decoding are both inference concepts. Greedy decoding always picks the single highest-probability next token. It is deterministic, fast, and often dull.
Is Beam Search considered intermediate?
Beam Search is generally considered intermediate-level material in the AI and LLM space.