LLM Decoding Strategies

At each generation step, the model produces scores over possible tokens. Greedy decoding selects the highest-scoring token; sampling draws from a distribution, often after temperature scaling or filtering. Top-k retains a fixed number of candidates, while top-p retains a set whose cumulative probability reaches a threshold. Beam search keeps several partial sequences and compares their scores across steps. These choices interact with stopping rules, repetition controls and the model's training. Provider APIs may expose only a subset, and similarly named parameters need not imply identical implementations.

A practitioner chooses decoding settings by measuring the application's desired behavior across representative inputs. Extraction may prioritize stable schema adherence, while brainstorming may value varied usable options. The configuration should record the model, seed support, token budget, stop conditions and sampling parameters. Repeated trials reveal variation that a single attractive answer conceals. Evaluating both failed generations and successful ones helps distinguish a model limitation from settings that encourage repetition, truncate an answer or suppress valid alternatives too aggressively.

Lower temperature does not make an answer true or universally deterministic. Hardware, model changes, hidden service settings and ties in token scores can still produce variation. Increasing diversity can help search but also increase errors and review cost. Beam scores are model likelihoods, not factual quality scores. Decoding experiments therefore need task-level checks, and a parameter that works for one model or objective should be retested when either changes.