Did I (she) or I (he) buy this? Or rather I (she/he)? Towards first-person gender neutral translation by LLM s (2025)

Abstract: This paper presents an analysis of gender in first-person mentions translated from English into two Slavic languages with the help of three LLMs and two different prompts. We explore if LLMs are able to generate Amazon product reviews with gender neutral first person forms. Apart from the overall question about the ability to produce gender neutral translations, we look into the impact of a prompt with a specific instruction which is supposed to reduce the gender bias in LLMs output translations. Our results show that although we are able to achieve a reduction in gender bias, our specific prompt cause also a number of errors. Analysing those emerging problems qualitatively, we formulate suggestions that could be helpful for the development of better prompting strategies in the future work on gender bias reduction.

## introduction it is known that machine translation (mt) systems, large language models (llm) and other natural language processing (nlp) applications are prone to bias, for instance gender bias (preference or toward one gender over the other). gender bias exists not only in training data and embeddings, but also algorithms themselves @xcite @xcite and in human annotations @xcite , so that an nlp system can produce gender biased predictions. since llms perform well for translation-related tasks, we try to test if we can reduce the gender bias by asking for a gender neutral translation in the prompt. we focus on the analysis of first-person gender in different translation variants of amazon product reviews. these variants were produced by three different large language models using two different prompts: the one approximating a holistic approach in translation, and the other with a specific instruction. the specific instruction is aimed to bring llms to use gender neutral forms. we analyse the llm translations from english into croatian and russian. the underlying texts are product reviews and contain particularly many words in first person singular. in contrast to english, both slavic target languages under analysis have gender marking not only on pronouns, but also on nouns, adjectives, verbs, determiners and numbers, see section 3.2 for more details. therefore, the gender which is not specified in the source needs to be specified in the target. when translating user reviews, the gender form of adjectives and verb past tenses and participles and passive constructions should be specified: купил (masculine for bought) vs. купила (feminine for bought). that is why it is difficult to completely avoid the first-person gender -it can be done only in a very small number of cases. the best way to provide gender neutral text is to use an inclusive form which includes both masculine and feminine gender: купил(а)bought (m/f). as it was shown in our previous work @xcite , both human and machine translated texts contain gender bias. moreover, machine translations tend to contain errors or inconsistencies related to these language contrasts and the need for specification in translation. the authors also reported on the usage of inclusive forms in translations generated with an llm. although gender bias has been thoroughly analysed in the recent machine translation studies, especially in translations with llms (see section 2 below), there are still not so many works addressing the impact of prompts onto gender bias and the problems that may additionally emerge. to the best of our knowledge, this is the first work on this type of languages addressing first person verb forms. for this reason, it is still not clear what kind of examples and more elaborated instructions for few-shot (or more complex) prompting would be the best option. therefore, in this work we investigate two zero-shot prompts: a holistic one and one with instruction to use gender neutral form, to better understand the behaviour of the models. furthermore, we examine the nature of systematic errors in the outputs which should help to design better prompt strategies for the future work. an analysis of error types for different models is not in the focus of the work. in our analysis, we focus on comparing the results of two prompt variants trying to answer the following research questions: rq1: are there differences in the outputs of different llms regarding the choice of the first person gender? rq2: can the more specific zero-shot prompt asking for a gender neutral translation decrease the bias? rq3: what are the problems with the zero-shot prompt and how the future prompts should be formulated to avoid it? the remainder of the paper is organised as follows: section 2 provides an overview of related studies. section 3 gives details on how translations were generated and how we performed the analysis. we present the results in section 4 and conclude providing outlook for future work directions in section 5. ## related work as already mentioned above, gender bias has attracted attention in machine translation studies in the recent years. for instance, last editions of the eamt conferences hosted dedicated workshop series @xcite with many works analysing not only neural machine translation (nmt), but also llm outputs. @xcite analyse outputs of various llms comparing their performance with neural mt models with a focus on gender bias for the language pairs english-spanish and english-catalan. they state that llms exhibit a higher degree of bias in comparison to nmt models. similarly to our aims, they try to eliminate the bias testing various prompting techniques testing the outputs on the winomt test set @xcite . in this way, they obtain gender-bias scores for each prompt. the prompt with the most considerable reduction in bias is then evaluated on the remaining test sets. the resulting prompt with the best performance achieves a reduction by 12%. @xcite use three few-shot prompting strategies with the aim to produce genderneutral translations. their prompts include both simple templates with examples not containing verbalised instructions and chain-of-thought step-bystep templates. the authors then perform manual analysis of translations with gpt4 for the english-italian language pair. their fine-grained analyses demonstrate promising results in prompting generative models for less gender-biased translations. the same language pair (english-italian) is also in focus of gender analysis by @xcite . the author analyses translations produced with chatgpt to see how and to what extent chat-gpt can handle gender. she also tests different prompts including one with a specific instruction to produce all possible alternatives in terms of gender. the results show that chatgpt often fails to generate feminine or binary gender translation alternatives. besides that, the resulting translations contain various errors. some publications are prompting llms for gender inclusive forms. nunziatini and diego (2024) report on using llms for post-editing mt for the english-spanish language pair in terms of gender inclusiveness. according to their results, gpt4 can be used for this task. another study @xcite uses gender-inclusive neomorphemes to improve machine translation in terms of gender inclusiveness, again for the english-italian pair. the authors test several llms and various prompting strategies including zero-shot and fewshot templates. it is also interesting to test if llms can be prompted for producing specific gender, as it is done by @xcite who explore the ability of llms to produce gender-specific translations. in general, some languages or language pairs are more prone to the problem of gender bias. most of the existing works explore how to totally avoid gender in english, which is easier than for many other languages @xcite . the problem already emerges even for closely related target languages such as german @xcite . in fact, the more categories with explicit gender marking a language has, the more problematic it is. most of the studies cited above deal with nouns and noun phrases describing professions or animate subjects. there are not so many studies focusing on other categories, for instance, first person constructions. @xcite propose automatic generation of both gender variants for the first person in arabic nmt translations, before the emergence of llms. in our previous work @xcite we investigated first person gender in the same two target languages, croatian and russian, however the focus was predominantly on the differences between human and machine translations. furthermore, we only included gpt3.5 language model, all other automatic translations were generated by nmt systems. for gpt3.5 translations we used the simple prompt "translate into target language" in order to provide the same instruction as to human translators. the results show that even with this simple and general prompt, gpt3.5 often generated inclusive forms with both variants, especially for the english-croatian translations. since some of the previous studies cited above show that prompting llms to generate more inclusive forms or gender-neutral forms is promising, in this work, we aim to compare the outputs of various models prompted with either simple or specific instructions for translation of the first person forms from the same english data into croatian and russian. we expect that a prompt formulated specifically for gender-neutral translation will result in a higher number of such gender-neutral translations when compared to the outputs of a simple prompt. ## results the results are presented in figure 1 , where we display distributions of the different gender labels for each of the translation outputs. as previously mentioned, the results for the gpt3.5 translation from @xcite are also included for the sake of comparison. grey colour indicates reviews without firstperson gender while orange indicates reviews with a gender inclusive form. those two categories are considered to be without gender bias and are the focus of the work. for both categories, lighter nuances denote that some error ocurred related to the first-person gender. in red, blue and violet reviews, the system did not generate any word in the inclusive form, but only in single gender form: red in feminine, blue in masculine, and violet with mixed genders or with errors. ## conclusion in this work, we examined three language models and their ability to generate a review translation from english into two slavic languages, croatian and russian, in which first person forms are used in gender-neutral or inclusive manner. we investigated two prompts, i.e. a simple holistic prompt, which corresponds to the translation brief for human translations, as well as a zero-shot prompt with a verbalised instruction to produce a gender-neutral translations for the first person forms. our overall result is that the gender-neutral prompt increases the percentage of inclusive variants for both languages and all models, however, with a number of errors. we quantitatively analysed various solutions, as well as the emerging errors. the nature of these errors was then qualitatively examined. as an outcome, we formulate a number of recommendations for more specific prompts that can be used to eliminate the possible errors: we plan to extend our prompting strategies following these recommendations. although our research is restricted to two languages only, we believe that these recommendations will be useful for other slavic languages too. besides that, these recommendations could also be valuable for other languages with explicit firstperson gender marking. we also believe that our findings will be useful not only for translation tasks with llms, but also for other language generation tasks for texts and languages with similar properties, e.g. those containing first person mentions in highly gendered languages.