---
license: apache-2.0
task_categories:
  - text-classification
  - translation
language:
  - en
  - es
  - de
  - ja
tags:
  - i18n
  - localization
  - translation-quality
  - llm-as-judge
  - benchmark
pretty_name: UIStringBench
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: public
        path: data/public.jsonl
---

# UIStringBench

**A benchmark for detecting translation errors in software UI strings.**

Machine and LLM translation of locale files fails in ways generic MT benchmarks don't measure:
dropped or invented placeholders, collapsed plurals, and — hardest of all — fluent translations
that state something the source doesn't (the Spanish that says *"could not be saved"* for
*"were saved successfully"*). As of September 2026 there is no standardized public benchmark for
this error class. UIStringBench is a first attempt at one.

## What's in it

Each row is a source/translation pair from the software-UI domain with a gold label:

| field | description |
|---|---|
| `id` | stable pair id |
| `lang` | target language (`es`, `de`, `ja`) |
| `source` | English source string |
| `translation` | candidate translation |
| `label` | `clean` \| `mistranslation` \| `omission` \| `addition` \| `glossary` |
| `split` | `public` |

**Public split: 228 pairs** — 168 clean (drawn from real shipped production locale files, including
adversarial pairs whose content looks like instructions or JSON), 54 planted errors (18 per
category), and 6 glossary violations (deterministically checkable against the included
`glossary.json`; no model involved).

**Held-out split: 60 pairs, private.** Kept out of this repository as a contamination defense;
leaderboard results report both splits separately. Composition mirrors the public split
(42 clean / 6+6+6 planted, no glossary items).

## Provenance — why you can trust the labels (and how far)

- The public corpus was committed to git (`Shipi18n/shipi18n@60d699b`, 2026-08-16) **before the
  judge that consumes it was written**, and the evaluation thresholds were fixed before
  implementation. Pre-registration is the credibility spine of this dataset.
- Labels were **not** revised after seeing model results. In published runs, roughly a quarter of
  the judge's "false positives" turned out to be the judge being right about pairs mislabelled
  `clean` — they are still counted against the judge, because a corpus you edit after seeing
  results is not a corpus.
- Honest limitations: three language pairs; one domain (UI strings, mostly from one product's
  site); corpus and held-out split authored by the tool's maintainer with LLM assistance. Planted
  errors are deliberate and unambiguous — real-world drift is subtler. Treat scores as an
  engineering QA signal, not a validated academic metric (the GEMBA authors' caution applies
  here too).

## Related work

- **GEMBA-MQM** (Kocmi & Federmann, WMT 2023) and successors (RUBRIC-MQM, GEMBA v2): LLM-as-judge
  MT evaluation on generic sentence pairs. UIStringBench differs in domain (locale files:
  placeholders, plurals, short ambiguous strings) and in shipping as a runnable dev-tool harness.
- **Last Translation Benchmark**: live, crowd-sourced hard-to-translate examples — general MT,
  not UI strings or structural failure modes. Adjacent, not overlapping.
- **COMET/xCOMET**: learned MT metrics on sentence pairs; multi-GB downloads, no locale-file
  awareness.

## Running it

The harness lives in the [Shipi18n repo](https://github.com/Shipi18n/shipi18n) under
`evals/semantic/`:

```bash
git clone https://github.com/Shipi18n/shipi18n && cd shipi18n
JUDGE_MODEL=<your-model> node evals/semantic/run.mjs
```

The judge design being measured: 3-pass majority vote, MQM-style category output
(mistranslation / omission / addition) rather than scalar scores, structural-first filtering.
Item-level results are published with each leaderboard release.

## Versioning

v1 (2026-09). New languages and formats (gettext, ICU plurals) will widen future versions;
saturated versions will be re-cut rather than silently edited. Maintainer: the
[Shipi18n](https://shipi18n.com) project. Issues and label disputes:
[GitHub issues](https://github.com/Shipi18n/shipi18n/issues).