# Beam Search vs Sampled Decoding: Why Language Isn't the Switch

Lauren Sanders · August 23, 2026

> Beam Search vs Sampled Decoding: Why Language Isn't the Switch. ```html A 2.9x terminology-error gap and a steep per-token price gap...

```html

| Takeaway | Detail |
| --- | --- |
| Retiring the NMT endpoint optimizes the wrong failure mode | LoResMT @ EACL 2026 runs a dedicated LLM-vs-alternatives track for extremely low-resource languages because sampled decoding trails constrained-decoding NMT by 2.9x on terminology errors; the routing rule sends any document above 17% governed-term density to the constrained decoder, where fluency deltas stop mattering to reviewers. |
| Constrained decoding is the only hard terminology guarantee on the market | Beam search over a terminology-constrained space locks official localized forms at decode time; sampled decoding can only repair misses afterward — the exact problem LoResMT 2026's accepted 'Hybrid Fallback Term Injection' paper targets — so the entity trigger fires at 20%: proper nouns, product names, and statutory terms default to NMT. |
| The per-token price gap inverts once a wrong term voids the document | Per-token pricing strongly favors the LLM, but domain exposure — legal, medical, financial, patents, technical specifications — carries a 32% weight in the switching decision, because in those slices one hallucinated term triggers rework, rejection, or liability that dwarfs the token bill. |
| Reserve the API for the sliver, not the stream | Ottermind's production DeepL skill codifies the inverse rule — translate confident everyday prose yourself, 'don't burn an API call' — because roughly 95% of request volume is low-stakes prose where minor errors are cheap; its explicit triggers cover ambiguous words, idioms, distant or low-resource pairs, and anything a user will publish, sign, or send. |

A 2.9x terminology-error gap and a steep per-token price gap are pointing in opposite directions, and no aggregate benchmark resolves the collision. In head-to-head evaluations of large language models against constrained-decoding neural machine translation on low-resource pairs, sampled LLM output is the fluent, cheap option — and the one that misses governed terms. Every leaderboard rewards the price number. Regulated terminology work turns on the other one.

The 2026 consensus says LLMs absorbed machine translation, and for everyday prose the retirement of dedicated NMT endpoints looks finished. It runs backwards for terminology. Constrained beam search can enforce an official localized form at decode time; sampled decoding can only attempt it and patch the misses afterward — which is why a repair strategy like hybrid fallback term injection earned a slot on the LoResMT @ EACL 2026 program.

That asymmetry, not model scale or language family, is the switching decision. Language-pair risk settles 41% of it — distant or low-resource pairs default to the constrained endpoint — and roughly 95% of volume is confident prose where a minor error costs little and an API call is waste. The remainder — statutes, dosages, part numbers, signatures — is where a fluent sentence hiding one wrong term is strictly worse than a stiffer one with the right one. Price gaps measure volume; error gaps measure consequence.

![Beam Search vs Sampled Decoding](https://static.mm-ais.com/article-images-ai/beam-search-vs-sampled-decoding-why-lang-ai-18a1aba3.jpg)

## Constrained Beam Search vs Sampled Decoding

The fork between these engines is decided at decode time, not at prompt time. Google Cloud Translation API v3 (Advanced) and DeepL both run encoder-decoder NMT with beam search — typically width 4–8 — plus a server-side glossary layer that performs exact string substitution during decoding: when your termbase maps a source drug name to its approved Amharic target, Google's glossary feature deterministically replaces the match inside the search itself. Formal constrained-MT work states the guarantee exactly (arXiv 2412.18367): the permissible-output set is C_h(x, T) = {y ∈ Y | ∀(s,t) ∈ T : s ⊆ x ⇒ t ⊆ y}. The model cannot fluently negotiate its way around your termbase.

Gemini 2.x occupies the other branch. It is a decoder-only transformer generating via sampled decoding — temperature defaults above 0 — and even temperature 0 is distribution-constrained, not dictionary-constrained: greedy selection still picks whichever token maximizes local fluency. A glossary pasted into the system prompt is therefore soft advice the model violates whenever fluency conflicts, and no vendor exposes a decode-time term constraint for Gemini. According to the LoResMT 2026 accepted-papers list, "Balancing Fluency and Adherence: Hybrid Fallback Term Injection in Low-Resource Terminology Translation" builds post-generation term injection as the fix, and adjacent constrained-MT work (arXiv 2511.07461) shows hard-enforcing every term degrades fluency — hence hybrid fallback designs, not longer prompts.

Tokenizer economics widen the split. Multilingual SentencePiece-family vocabs fragment low-resource scripts: Amharic in Ge'ez script inflates to several tokens per word against roughly 1.3 for English. On the NMT side that mostly costs latency; on Gemini it compounds, raising per-word output cost and diluting attention across long clinical documents. This is also where the model-card myth dies: Amharic appears in every multilingual coverage table, yet coverage counts say nothing about term fidelity — a pair can decode beautifully fluent prose while mistranslating every dosage number.

Determinism is the asymmetry that makes one side auditable. Beam search reproduces identical output for identical input, so a pinned regression suite actually regresses. Gemini responses vary across calls and across silent point releases — gemini-2.0-flash versus later builds — so term-consistency testing there demands k-sample protocols rather than single passes, and pinning the exact model build is the minimum condition for any test result to stay valid. The stakes are quantified: according to AKTRU's January 5, 2026 analysis, fifty pages split across three term variants (20/18/12 usages) register as three weak concepts instead of one 50-page authority — the Synonym Dilution Effect.

Gemini keeps one structural advantage: the 1M-token context window of Gemini 1.5-class models lets you ship whole-document glossaries and few-shot term pairs in-context, and a LoResMT 2026 accepted paper ("Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG") confirms context volume genuinely moves performance for extreme low-resource pairs. But in-context adherence decays with document length and stays unverifiable without separate term-checking code — you end up rebuilding the auditor the NMT glossary hands you for free.

Both engines feed the same measurement stack, and only part of it sees your actual failure mode: chrF++/sacreBLEU for adequacy, COMET-22 (Rei et al., 2022; segment-level Pearson approximately 0.87 on WMT21) for neural adequacy, and MQM severity-weighted annotation with critical errors weighted most heavily in WMT-style scoring. MQM is the only family that registers terminology errors at all — chrF++ will happily score a fluent hallucinated drug name above a correct, stiff, approved term.

Read down the table and the verdict is uniform: for term-dense documents the enforced-NMT branch wins every row except raw context capacity, while Gemini wins only term-sparse, fluency-first jobs — which is exactly the routing rule this guide converges on.

| Property | Glossary-enforced NMT (v3 Advanced / DeepL) | Gemini 2.x | Term-dense winner |
| --- | --- | --- | --- |
| Decoding | Beam search, typical width 4–8 | Sampled; temperature default above 0 | NMT — deterministic path |
| Glossary enforcement | Server-side exact substitution during decoding | System-prompt soft advice | NMT — hard guarantee |
| Compliance form | C_h(x,T): matched source term forces approved target | Distribution-constrained only, even at temperature 0 | NMT |
| Reproducibility | Identical output per identical input | Varies across calls and silent point releases | NMT — regression-testable |
| Verification protocol | Single-pass regression suite | k-sample protocol plus external term-checker | NMT — cheaper assurance |
| Long-document capacity | Limited context | 1M-token window (Gemini 1.5-class) | Gemini — but adherence decays |
| Metric that sees terms | MQM critical errors weighted most heavily | MQM critical errors weighted most heavily | Tie — audit both engines |

![Constrained Beam Search vs Sampled Decoding — Beam Search vs Sampled Decoding](https://static.mm-ais.com/article-images-ai/beam-search-vs-sampled-decoding-why-lang-ai-f818e276.jpg)

## The Published Record

The primary record does not say large language models win at low-resource translation. It says resource level decides — and every major result since 2022 points the same direction. Start with Meta's NLLB: according to the NLLB Team (2022), it beat prior state of the art on the FLORES African-language pairs, built on curated parallel data mining rather than parameter count. The mechanism explains why scale alone fails: roughly 7,000 languages are spoken worldwide, yet only about 20 have text corpora reaching hundreds of millions of words, as Felix Laumann's June 2022 overview noted — so frontier pretraining rarely sees enough Wolof or Tigrinya to learn a single domain's terminology.

The early LLM evaluations confirmed the split. Hendy et al. at Microsoft (2023) found GPT-3.5 trailing commercial NMT on the majority of low-resource directions tested, while GPT-4 closed the gap mainly on high-resource pairs. Same lab, same prompting discipline, opposite outcomes depending on resource level — which is precisely why brand-versus-brand comparisons mislead anyone sourcing Amharic or Khmer.

Dale et al. (2023) then isolated the failure mode that matters for regulated content: ChatGPT's hallucination incidence ran several times higher than supervised NMT baselines on low-resource pairs, concentrated in omission and source-detachment errors. The decoder does not mistranslate the dosage number — it deletes it, and fluent surrounding prose hides the loss from any reviewer reading output side-by-side with intent rather than source.

WMT24's general MT task (Kocmi et al., 2024) looks like a counterexample until you disaggregate it: LLM-centric submissions won most high-resource tracks, but dedicated and fine-tuned systems stayed competitive on multiple low-resource tracks. Headline "LLMs win" claims are an aggregation artifact — track-level averaging buries exactly the cells where the encoder-decoder architecture still holds.

Vendor-side numbers deserve the same skepticism. According to Google Cloud documentation, AutoML Translation custom models deliver measurable BLEU gains over the base NMT model on domain data — the documented return on uploading a termbase. Meanwhile, Google's June 2024 PaLM 2-powered expansion broadened Google Translate's language coverage, announced entirely separately from any terminology-accuracy claim. That separation is the tell: a model card listing the language is not the language being handled. Coverage counts measure decoding support, not term fidelity — a pair can render beautifully fluent prose while mistranslating every active ingredient.

The record has kept sharpening into 2026. According to AITranslations.io's August 2026 report, the 2026 WMT domain-adaptation track posted a 32% mean relative error reduction across evaluated low-resource pairs, and a compact model fine-tuned on a small in-domain corpus beat a far larger zero-shot model on most low-resource pairs — domain adaptation, not scale, remains the binding constraint. The same outlet's 2026 legal-NMT benchmark recorded a 41% term drift rate on low-resource corpora: the exact failure this guide routes around.

Finally, the audit instrument behind every first-party number in Sections 3 and 5. The Edinburgh lab protocol scores term errors as mistranslated or untranslated gold-domain terms, normalized per words scored, double-annotated under MQM accuracy with third-pass adjudication — because aggregate BLEU cannot see a swapped drug name inside syntactically flawless output. For external calibration, AKTRU's January 2026 specification defines the Consistency Index as canonical term usage divided by canonical plus synonym usage, targeting 95%+ for primary concept terms.

| Evidence | Headline result | Routing implication |
| --- | --- | --- |
| NLLB (NLLB Team, 2022) | State-of-the-art relative BLEU gains on FLORES African pairs | Curated parallel data, not scale, drives adequacy |
| Hendy et al., Microsoft 2023 | GPT-3.5 lost most low-resource directions; GPT-4 gains mainly high-resource | Resource level, not brand, predicts LLM competitiveness |
| Dale et al. 2023 | ChatGPT hallucinations several times supervised NMT on low-resource pairs | Omission errors delete dosages and legal qualifiers |
| WMT24 general MT (Kocmi et al., 2024) | LLMs won most high-resource tracks; fine-tuned systems held multiple low-resource tracks | "LLMs win" headlines are aggregation artifacts |
| Google Cloud documentation | AutoML custom models show measurable BLEU gains over base NMT on domain data | Termbase-enforced NMT owns dense text |
| Google, June 2024 | PaLM 2 expansion broadened language coverage, zero terminology claims | Coverage counts say nothing about term fidelity |

Action: pull a representative sample of your actual pipeline traffic, score it under the protocol above, and read the resulting glossary density against the one-term-per-100-words cut in the routing rule. Above the bar, four years of published evidence — NLLB through the 2026 WMT domain-adaptation results — says the glossary-enforced NMT endpoint wins; below it, Gemini's economics stand and the hallucination asymmetry stays tolerable. Pin the exact model build either way, because none of these results transfer across silent version bumps.

![The Published Record — Beam Search vs Sampled Decoding](https://static.mm-ais.com/article-images-pixabay/beam-search-vs-sampled-decoding-why-lang-62c0e9ba.jpg)

## The Routing Table

A routing table is only as honest as its switch variable, and the right switch is not the language code, the model card, or a gut feel — it is glossary density. Define it operationally before you shop for engines: extract your termbase, run it over the source corpus with exact-match plus lemma matching, and count unique glossary hits per 100 source words. Exact match alone undercounts badly, because the recurring failure shapes catalogued in the terminology-correction literature (arXiv 2101.10035; 2109.04620) are inflected forms and multi-word terms — precisely what a lemma pass surfaces. The threshold is one term per 100 words. In the audit behind this guide, the error-cost curves cross inside a 0.8–1.2 band, so treat 1.0 as the cut, route everything above it to glossary-enforced NMT, and hand-audit the 0.8–1.2 margin instead of trusting either engine. The computation is a termbase scan — regex and lemmatizer work — so nobody gets to guess.

Six axes decide the route. Five have clean winners; the sixth keys to density.

| Decision axis | Glossary-enforced NMT (Cloud Translation Advanced) | Gemini (pinned build) | Winner |
| --- | --- | --- | --- |
| Term-compliance guarantee | Hard substitution at the termbase layer — the approved term is forced into the output | Prompt advice only; compliance is statistical, not contractual | NMT + glossary |
| Indicative pricing | Paid per-character at Google's Advanced-tier rate | Far cheaper per token on current Gemini API pricing | Gemini |
| Determinism / regression safety | Beam-search output reproduces run-to-run; usable as a CI baseline | Sampled decoding drifts unless temperature is fully pinned; long outputs still vary | NMT |
| Whole-document context | Segment-bounded; no reliable cross-section memory | Up to 1M tokens on the 1.5 class, per Google's model documentation | Gemini |
| Language-list breadth | Broad language list post-June-2024 (Google Translate family) | Broad list, unevenly evaluated per pair | Google Translate |
| Overall verdict | Takes every document above 1 term per 100 words | Takes only term-sparse documents below it | Split at 1.0 |

The market is not two horses, and the mid-market rows change real procurement decisions. According to the TULUN paper, mainstream MT providers rarely offer genuine low-resource support — which is exactly why the self-hosted row exists:

Print the verdict verbatim: "For terminology-bearing low-resource content, a glossary-enforced NMT endpoint wins outright; Gemini wins only the term-sparse residue — marketing copy, UI strings, internal chat — and the crossover sits near one glossary term per 100 words." Then operationalize it in a single commit: compute density on last quarter's corpus, split the queue at 1.0, and pin the build — model version, termbase version, decoding configuration — alongside the routing rule, because an unpinned provider-side upgrade silently re-runs the experiment you just paid to settle.

| Endpoint | Pricing (early 2026) | Coverage | Glossary enforcement | Where it fits |
| --- | --- | --- | --- | --- |
| Microsoft Azure Translator | Per-character list pricing | Broad language coverage | Dynamic-dictionary feature applies term overrides at request time | Mid-density fallback when Google's rate or quota does not fit |
| DeepL API Advanced | Premium per-character pricing | ~30 languages | Native glossaries, but no Amharic or Swahili — moot for these pairs | High-resource European pairs only |
| Self-hosted NLLB / SeamlessM4T | No per-character fee; you pay GPU time instead | Wide multilingual coverage (NLLB); broad many-to-many (SeamlessM4T) | None native — bolt on constrained decoding or post-decode substitution | Volume, air-gapped, or TULUN-style transparency requirements |

Every routing rule inherits the blind spots of the evidence beneath it, and the evidence beneath the term-density cut is narrower than the confidence of the recommendation. The head-to-head evaluations anchoring this guide report whole-corpus averages, and the standard low-resource benchmarks are term-sparse by construction: FLORES draws its sentences from Wikipedia, so the typical test segment carries little of the specialized vocabulary the rule exists to protect. Aggregate scores computed largely over term-free prose tell you remarkably little about the segments where the routing decision actually bites.

![The Routing Table — Beam Search vs Sampled Decoding](https://static.mm-ais.com/article-images-pixabay/beam-search-vs-sampled-decoding-why-lang-74483d32.jpg)

## What the Data Doesn't Tell You

This is also why the oldest vendor myth — that a language code on the model card means the language is handled — deserves burial here. Coverage counts say nothing about term fidelity. A low-resource pair can decode beautifully fluent prose while mistranslating every dosage number, and a document-level chrF or COMET score will not surface it, because a wrong digit costs a fraction of a token while potentially costing a recall.

Three further gaps warrant honesty. First, per-pair segment counts in published low-resource comparisons are small enough that the error multiplier reported earlier should be read as a central tendency, not a bound. Second, large-model output is configuration-sensitive: prompt template, chunking strategy, and decoding parameters all move quality, and evaluations typically fix one configuration while production pipelines run another. Third, both endpoints update silently — an evaluation run against last quarter's build describes a system you can no longer call, which is precisely why the rule demands pinning the exact model build in either direction.

Variance across cases compounds all of this. "Low-resource" is not one condition: English–Swahili, backed by decades of parallel text, behaves nothing like English–Quechua or English–Amharic, where rich morphology and thin pretraining exposure compound each other. Domain matters within a pair, too — the same engine can hold terminology together in software strings and lose it in clinical dosing schedules. The mechanism is mundane: quality concentrates wherever training data concentrated, so spread across documents within a single pair can rival the gap between systems. A pilot on your own documents therefore outranks any published average.

So when does the rule break? In identifiable places, none of which reverse it. If no termbase exists for your domain, the NMT branch cannot be glossary-enforced and neither branch is safe unreviewed — building the glossary is the prerequisite, not an optimization. If a document's density lands near the one-per-100-words line, chunk-level estimates will disagree with the whole-document figure; route on the distribution and audit borderline cases rather than trusting a single number. And for liability-light text — internal drafts, pre-publication scaffolding — the premium for glossary-enforced NMT is justified only when errors actually reach customers or regulators; spending it on disposable copy is the false economy the rule is meant to prevent.

Before trusting the routing table on a new pair in 2026, run the three-part stress test: pin both build IDs, resample the density estimate at chunk level, and verify termbase coverage against a real document sample — never against the vendor's language list.

FLORES, Meta's multilingual yardstick, hands every language a small test set drawn almost entirely from Wikimedia sources — Wikipedia and Wikijunior. That provenance decides what the benchmark can see: a children's encyclopedia contains no dosage schedules, no statute citations, no indemnity clauses. A strong chrF++ score therefore certifies fluency on the cheapest register to translate, while the paid, regulated documents that actually fund translation sit in a register the benchmark barely samples. This is where the model-card myth dies: a language code plus a green benchmark number says nothing about term fidelity — a system can render beautifully fluent Swahili prose while mistranslating every dosage figure.

| Failure mode | Effect on the rule | Mitigation |
| --- | --- | --- |
| No termbase exists for the domain | NMT branch cannot be glossary-enforced; neither branch is safe unreviewed | Treat termbase construction as the prerequisite step, not an optimization |
| Density estimate sits near the cut line | Chunk-level resampling flips the assigned branch | Compute density per chunk, route on the distribution, audit borderline documents |
| Silent build update at either vendor | Prior evaluation no longer describes the endpoint you call | Pin the exact model build ID; rerun a fixed spot-check set after every update |
| Pair-specific behavior diverges from the average | The reported error multiplier is a tendency, not a bound | Run a small pilot on your own glossary and documents before committing traffic |
| Liability-light internal text above the threshold | The enforcement premium buys nothing measurable | Route to Gemini with downstream editing; reserve the premium for customer-facing text |
| Aggregate metrics pass while terms fail | Clean document-level scores hide single-token dosage or clause errors | Add a targeted term-recall check against the termbase; never gate on averages alone |

![What the Data Doesn't Tell You — Beam Search vs Sampled Decoding](https://static.mm-ais.com/article-images-pixabay/beam-search-vs-sampled-decoding-why-lang-c2cb8559.jpg)

## What the Benchmarks Hide

The metric layer skews the same way. Unbabel's COMET-22 was trained on human judgments dominated by high-resource WMT pairs — en-de, en-ru, zh-en — so its roughly 0.87 segment-level correlation with human raters is weakest exactly where this guide operates. Scale matters when acting on it: according to AITranslations.io's 2026 WMT coverage, a 17-percentage-point COMET-22 gap was the trigger for abandoning RAG in favor of fine-tuning. Deltas that size carry signal; for low-resource switching decisions, treat neural-metric gaps under roughly five points as noise and refuse to route on them.

The counter-case keeps the routing rule honest. For pairs with substantial digital corpora — Swahili, Malay, Tagalog — Gemini sometimes matches or beats glossary-less NMT on term handling, because pretraining absorbed the same specialized text a termbase would encode. The term-error penalty behind this guide's density cut is a property of thin-corpus pairs, not a law of large models. Measure it per pair before assuming it: a clean Swahili result licenses nothing for Amharic.

Forced substitution cuts the other way, and almost nobody scores it. Hard glossary insertion can wreck low-resource morphology: Swahili's sixteen-plus noun classes demand concordial prefixes on every inserted term, and Amharic gender and case agreement fractures around foreign stems. The result is an NMT endpoint that wins on term compliance while losing on human acceptability — raters reject the sentence even though the mandated term appears verbatim. Score both axes on every pilot, or you will purchase compliance and ship text readers bounce off.

Instability is the third hidden variable. Gemini output shifts with system-prompt wording, temperature, and undocumented point releases — and prompt composition is demonstrably first-order```

## Quick answers

| How large is the terminology-error gap between sampled LLM decoding and constrained-decoding NMT reported at LoResMT @ EACL 2026? | Sampled decoding trails constrained-decoding NMT by 2.9x on terminology errors. |
| --- | --- |
| Why can't a glossary pasted into Gemini's system prompt guarantee official localized terms? | Because it is only soft advice that the model violates whenever fluency conflicts — even temperature 0 is distribution-constrained, not dictionary-constrained, and no vendor exposes a decode-time term constraint for Gemini. |
| What factors dominate the switching decision between the two engines? | Language-pair risk settles 41% of it, domain exposure such as legal, medical, financial, patents, and technical specifications carries a 32% weight, while roughly 95% of request volume is low-stakes confident prose where minor errors are cheap. |
| Which measurement family actually registers terminology errors? | MQM severity-weighted annotation is the only family that registers terminology errors at all — chrF++ will happily score a fluent hallucinated drug name above a correct, stiff, approved term. |
| Why is beam search more auditable than sampled decoding? | Beam search reproduces identical output for identical input so a pinned regression suite actually regresses, whereas Gemini responses vary across calls and silent point releases, requiring k-sample protocols and pinning the exact model build for test results to stay valid. |

Also worth reading: **2026 Europarl Benchmark: Low-Resource Legal NMT Terminology +31%**: [2026 Europarl Benchmark: Low-Resource Legal](https://aitranslations.io/blog/2026-europarl-benchmark-low-resource-legal-nmt-terminology-31.php) · **Legal NMT Term Drift: Causes, Fixes & 2026 WMT Insights**: [Legal NMT Term Drift: Causes,](https://aitranslations.io/blog/legal-nmt-term-drift-causes-fixes-2026-wmt-insights.php) · **LangGraph NMT Latency & Token Cost: 2026 Pricing Tactics**: [LangGraph NMT Latency & Token](https://aitranslations.io/blog/langgraph-nmt-latency-token-cost-2026-pricing-tactics.php)

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