Can Explicit Gender Information Improve Zero-Shot Machine Translation? (2025)

Abstract: Large language models (LLMs) have demonstrated strong zero-shot machine translation (MT) performance but often exhibit gender bias that is present in their training data, especially when translating into grammatically gendered languages. In this paper, we investigate whether explicitly providing gender information can mitigate this issue and improve translation quality. We propose a two-step approach: (1) inferring entity gender from context, and (2) incorporating this information into prompts using either Structured Tagging or Natural Language . Experiments with five LLMs across four language pairs show that explicit gender cues consistently reduce gender errors, with structured tagging yielding the largest gains. Our results highlight prompt-level gender disambiguation as a simple yet effective strategy for more accurate and fair zero-shot MT.

## introduction large language models (llms) have exhibited impressive capabilities in zero-shot machine translation (mt) by leveraging cross-lingual patterns acquired during pretraining @xcite . however, these models also inherit and propagate societal biases present in their training data, leading to systematic gender mistranslations @xcite . this issue is especially pronounced when translating from languages without grammatical gender into those with gendered grammatical systems @xcite @xcite . gender bias in llm-based mt can be observed when models incorrectly assign gender in translations, even when the source sentence provides sufficient contextual clues to infer the correct gendered form @xcite . for instance, given the sentence, "the carpenter built the attendant a desk ## related work gender bias has been shown issues to various fields in natural language processing @xcite under different settings and tasks, i.e from foundation model @xcite @xcite to specific tasks question answering @xcite , coreference resolution @xcite and others @xcite . in the era of large language models (llms), the research community has analyzed the impact of gender bias @xcite and proposed several mitigation strategies. these include parameter-based approaches such as fine-tuning @xcite , controlled decoding @xcite , and model editing @xcite , as well as promptbased methods like using specially designed structures, i.e. chain-of-thought prompting, in-context learning, etc. @xcite @xcite in the field of mt, gender bias has been shown to negatively affect translation quality @xcite @xcite , often leading to incorrect or stereotypical gender representations in target languages @xcite @xcite . to support research in this area, several benchmark datasets and evaluation resources have been developed to facilitate systematic analysis of gender-related translation errors @xcite . in response, various mitigation strategies have been proposed, with a main focus on finetuning, balancing genders in dataset, adaptive learning method and prompting (escudé font and costajussà, 2019; costa-jussà and de jorge, 2020; saunders and byrne, 2020; @xcite . ## our approach this study addresses the challenge of translating source sentences from languages without grammatical gender (e.g., english) into target languages that exhibit grammatical gender distinctions (e.g., german). specifically, we focus on cases involving gender-unambiguous entities, those whose gender can be reliably inferred from contextual information present within the sentence itself. we define a gender-unambiguous entity as one for which the grammatical gender can be deduced unambiguously from intra-sentential cues. for instance, consider the english sentence in figure 1 : "the carpenter built the attendant a desk as a gesture of her love.". in this example, the pronoun her clearly identifies the referent carpenter as female. an accurate and contextually faithful translation into a gendered target language should appropriately reflect this gender information in its lexical and grammatical choices. we employ a basic prompting template for zeroshot mt. the template is structured as follows: "translate the following sentence from src_lang to tgt_lang:\n[src_lang]: a\n[tgt_lang]: " in this format, src_lang and tgt_lang denote the source and target languages, respectively, while a represents the input sentence to be translated. an illustrative example of this prompting strategy is provided in the upper portion of figure 1 . in contrast to the baseline, our proposed two-step approach integrates explicit gender inference into the translation process. in the first step, we leverage the capabilities of llms to infer the gender of a target entity based exclusively on contextual information provided in the source sentence. let m denote a decoder-only model, primarily trained on english text. given m's deep understanding of english, we leverage it to predict the gender of the entity. we employ the following prompting template t : "you are an expert in english grammar and linguistic analysis, with no gender bias. based solely on the information provided in the context, determine the most likely gender (male or female) of the specified entity. here, a refers to the source sentence, and b denotes the entity of interest within that sentence. given the candidate set {male, female}, our objective is to accurately infer the gender of b based on contextual cues in a. for each gender candidate, we combine t with the gender candidate to create a full statement. this statement is then tokenized into n tokens: w 1 , w 2 , . . . , w n 1 , w n 1 +1 , . . . , w n . the first n 1 tokens come from t , while the rest are from the gender candidate. we calculate the perplexity only over the (n -n 1 ) tokens of the gender candidate in the full statement: here, p m (w i | w 1 , . . . , w i-1 ) is the probability of token w i given its preceding context as estimated by the model m. after computing the perplexity scores for both gender candidates associated with the entity b, we select the candidate with the lowest perplexity as the predicted gender: ĝ = arg min @xmath0,2} ppl(g j ). we incorporate the predicted gender information into the translation prompt, as illustrated in the lower portion of figure 1 , using two distinct formatting strategies: structured tagging and natural language. by explicitly including a single, highconfidence gender prediction, we aim to enhance the model's ability to accurately reflect the gender of the target entity during translation. in the structured tagging approach, the gender information is appended directly adjacent to the entity using bracket notation (e.g., carpenter (female)). in contrast, the natural language approach conveys the same information in the form of a natural language instruction, such as: "use the feminine form for 'carpenter'" for female referents, and "use the masculine form for 'carpenter'" for male referents. it is important to note that, following translation using the structured tagging method, we apply a post-processing step to remove bracketed gender annotations (e.g., "(female)" or "(male)") from the translated output. this is achieved through a simple heuristic based on dictionary lookup to identify and omit corresponding phrases in the target language, ensuring that the final translation remains natural and fluent. ## ablation study since comet scores show biases in recent reports @xcite , we assess whether the observed mt improvements are significant and meaningful in realistic scenarios by employing the llama-3.3-70b-instruct model as an automatic scorer or judge @xcite . comparative results between the base models and those incorporating gender information (+t and +n) are presented in we present an example illustrating the use of gender information in comparison to the base system in appendix a. ## conclusion this paper explored the use of explicit gender information to reduce gender bias in zero-shot mt and improve the translation performance. we proposed a two-step approach: first, leveraging llms to infer the gender of unambiguous entities from context; second, incorporating this information into translation prompts using either structured tagging or natural language formats. comprehensive experiments across five models and four language pairs demonstrate that explicit gender cues consistently improve translation quality, with structured tagging yielding the most significant gains.