Is greedy decoding the same as temperature zero?

Effectively yes for practical purposes: setting the sampling temperature to zero makes the model always choose the highest-probability token, which is exactly greedy decoding. This is why temperature zero is the standard setting for deterministic extraction. It is distinct from beam search, which also is deterministic but searches over multiple sequences rather than committing to the single most likely token at each step, and distinct from positive-temperature sampling, which introduces randomness on purpose.

Should I use beam search for JSON extraction?

Usually not. For short structured outputs the greedy path is almost always correct, so the extra compute of beam search rarely changes the result, and its bias toward bland sequences offers no benefit here. Reliability for structured extraction comes far more from schema enforcement, validation, and good prompting than from the decoding search strategy. Use greedy at temperature zero for deterministic, auditable JSON, and invest the effort in structured-output enforcement rather than in the decoder.

AI in Markets Comparison

Greedy vs Beam Search Decoding

Decoding turns a model's per-token probabilities into an output sequence, and the strategy affects determinism, quality, and cost. Greedy decoding commits to the most likely token at every step, never reconsidering. Beam search hedges by tracking several partial sequences, or beams, and at each step keeps the top combinations by cumulative probability, so it can recover from a locally tempting but globally worse choice. Both are deterministic, unlike temperature sampling. For the short, structured outputs typical of filing extraction, the practical difference is smaller than for long open-ended generation. This matrix compares them.

6 CRITERIAPublished May 26, 2026Live Content

By AI Fin Hub Research · AI Fin Hub Team

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Options 6 criteria Verdict FAQ

Greedy Decoding Option

Selects the single highest-probability token at each step with no lookahead. Deterministic, fast, and the simplest decoding strategy.

Pros

Fast and cheap: one forward path, no extra candidate sequences to maintain
Deterministic, giving reproducible output that aids debugging and auditing
Adequate for short, structured extraction where the high-probability path is usually correct
Simple to implement and reason about, the default for many extraction pipelines

Cons

Myopic: a locally best token can lead into a globally worse sequence with no recovery
Can get stuck in repetition loops on open-ended generation
No exploration of alternative phrasings, which occasionally matters for fluency
Cannot enforce a globally higher-probability sequence the way beam search can

Short structured extraction, deterministic and auditable output, and high-volume pipelines where speed and reproducibility matter

Beam Search Option

Maintains several candidate sequences in parallel and expands the highest cumulative-probability combinations, choosing the best overall sequence at the end.

Pros

Finds higher-probability sequences than greedy by considering several paths at once
Recovers from a locally tempting token that greedy would commit to and regret
Deterministic and useful for constrained generation where the global sequence matters
Beam width tunes the quality-versus-cost tradeoff explicitly

Cons

Several times the compute of greedy, scaling with the beam width
Tends toward bland, safe, or repetitive output, the well-known beam-search degeneration
Higher probability is not always better text, so it can underperform on open generation
Rarely helps for short structured extraction where the greedy path is already correct

Constrained generation and tasks where the globally most probable sequence matters, less so short structured extraction

Decision Table

See the tradeoffs side by side

Criterion	Greedy Decoding	Beam Search
Lookahead	None, token by token	Keeps several candidate paths
Determinism	Yes	Yes
Compute cost	Low	Higher, scales with beam width
Finds global optimum	No, myopic	Closer, still approximate
Output tendency	Direct, can loop	Bland, can repeat
Fit for short extraction	Good default	Rarely worth the cost

Verdict

For the extraction tasks that dominate finance LLM work, short structured outputs like a number, a date, or a small JSON object, greedy decoding at temperature zero is the right default: it is fast, deterministic, reproducible for auditing, and on these tasks the highest-probability path is almost always the correct one, so beam search's global search buys little. Beam search is genuinely useful when the globally most probable sequence matters and a single greedy misstep would derail the whole output, as in constrained generation, but it costs several times the compute, and its well-documented tendency to produce bland or repetitive text means higher sequence probability does not always mean better output. So reserve beam search for the narrow cases where global optimality clearly helps, and use greedy for everything else. Note that both are deterministic and distinct from temperature sampling and self-consistency, which deliberately introduce randomness to explore reasoning diversity; if you want exploration, that is a different axis than greedy versus beam.

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FAQ

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The short answers readers usually want after the first pass.

Beam search optimizes for the highest cumulative-probability sequence, but the most probable sequence is not always the best one. Models tend to assign high probability to safe, generic, and repetitive continuations, so aggressively maximizing probability can yield bland or looping output, a phenomenon known as beam-search degeneration. For short structured extraction this rarely bites because the target is unambiguous, but for open-ended generation it is a real failure mode and is one reason sampling-based methods are often preferred for creative text.

Sources & References

The Curious Case of Neural Text Degeneration — Holtzman et al., ICLR (2020)
Speech and Language Processing — Jurafsky and Martin (3rd ed. draft)

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