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Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures

Summary

Translating from English into languages with grammatical gender, such as Italian, forces a system to commit to masculine or feminine forms. Models often ignore explicit cues like pronouns and fall back on occupational stereotypes. Standard WinoMT-style accuracy shows these disparities but cannot tell whether errors come from ignoring context or from a built-in default.

The authors extend their earlier Minimal Pair Accuracy (MPA) framework in two ways:

  • Prior Bias: a new measure of which gender a model assumes when no cue exists. It is computed on a Neutral Set, a version of WinoMT where gendered pronouns become they/them and agreement is adjusted.
  • Decoder-only models: the framework is applied to three 7B models from one lineage (Llama2, TowerBase, TowerInstruct), using output-level evaluation and an attention-based analysis of cue integration.

The decoder-only LLMs generally do not beat earlier encoder-decoder systems on gender-specific metrics. However, post-training, especially instruction tuning, improves the use of contextual cues and lowers the masculine Prior Bias. This shows that scale alone does not fix gender bias, while training stages can.

Key points

  • Models compared: Llama2 (general-purpose baseline), TowerBase (continued pretraining on monolingual and parallel data) and TowerInstruct (instruction-tuned for translation). All share an architecture of 7B parameters, 32 layers and 32 attention heads.
  • MPA metric: a translation counts as correct only if both versions of a minimal pair (cue swapped, e.g. he/she) are translated with the right gender.
  • Neutral Set: a newly created variant of the WinoMT benchmark (Stanovsky et al., 2019) with gender cues removed, which makes Prior Bias measurable. Combined with MPA, it separates errors from ignored context, from default bias, or from both.
  • Setup and comparison: prompting is zero-shot to isolate model behavior rather than optimize prompts. Results are compared with the encoder-decoder results reported in Manna et al. (2025).
  • Attention analysis and availability: the attention analysis covers accurate minimal pairs (n = 195 in the secondary-entity analysis). Code and data are at github.com/chiaramanna/gender-bias-diagnostics-mt.

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: Linguistics & languages · Machine translation

Topics: Machine translation

Cited by

3 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 2 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/4wphxianzxf6

@inproceedings{manna2026gender,
  title = {{Gender Disambiguation in Machine Translation: Diagnostic Evaluation in Decoder-Only Architectures}},
  author = {Manna, Chiara and Mohebbi, Hosein and Alishahi, Afra and Blain, Frederic and Vanmassenhove, Eva},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/4wphxianzxf6},
  url = {https://doi.org/10.63317/4wphxianzxf6}
}

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