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On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation

Summary

Modern translation models often sample their outputs with a temperature setting, so the same input can produce different translations on different runs. The authors examine this non-deterministic behaviour (ND-MT) as both an opportunity and an evaluation problem. They sample multiple translations from 22 open systems on WMT23–24 news data in six directions. The systems include encoder-decoder models (mBART, NLLB-200) and decoder-only LLMs from the Llama-2/3, Qwen-2.5/3 and DeepSeek families. Outputs are scored with lexical and semantic metrics, plus a new reference-free group-level measure called GLVS.

At low temperature, sampled candidates vary in wording but keep the meaning. This helps with multimodality, the fact that one source sentence can have many valid translations. At higher temperatures meaning drifts, so the authors describe these systems as temperature-constrained. Evaluation, however, becomes unstable: system rankings shift depending on how candidate groups are aggregated. The weakest candidate per source tends to dominate the ranking, which the authors call a Buckets Effect. Their ExpectoSample strategy first identifies dependable metrics and then uses them to select robust systems.

Key points

  • The study covers 22 MT systems across six translation directions, using 11,947 source sentences from WMT23–24 data. Code, data and results are released at github.com/weichuanW/TC-DN-MT.
  • With a uniform temperature of 0.5, ND-MT outputs show clear lexical diversity while staying nearly identical in meaning to deterministic outputs from the same models. At higher temperatures, semantic equivalence is no longer preserved.
  • GLVS (Group Lexical Variance Score) is introduced as a reference-free, group-level lexical metric. It reduces the bias caused by having only a few references.
  • Applying D-MT evaluation via group statistics (min, max, mean, random, std) yields inconsistent rankings. Tests with five state-of-the-art systems at sample sizes of 10, 20 and 50 show that the worst candidate largely determines rankings (the Buckets Effect).
  • Stated limitations: mainly open-source models, a limited temperature range, no human evaluation, and only six language directions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Machine translation · Natural language processing

Topics: Machine translation

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.379

@inproceedings{wang2026temperature,
  title = {{On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation}},
  author = {Wang, Weichuan and Liu, Mingyang and Ma, Chen and Song, Linqi},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.379},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.379}
}

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