“ I Need More Context and an E nglish Translation”: Analysing How LLM s Identify Personal Information in K omi, P olish, and E nglish (2025)

Abstract: Automatic identification of personal information (PI) is particularly difficult for languages with limited linguistic resources. Recently, large language models (LLMs) have been applied to various tasks involving low-resourced languages, but their capability to process PI in such contexts remains under-explored. In this paper we provide a qualitative analysis of the outputs from three LLMs prompted to identify PI in texts written in Komi (Permyak and Zyrian), Polish, and English. Our analysis highlights challenges in using pre-trained LLMs for PI identification in both low- and medium-resourced languages. It also motivates the need to develop LLMs that understand the differences in how PI is expressed across languages with varying levels of availability of linguistic resources.

## introduction the lack of data for low-resourced languages is a known problem in computational linguistics. this problem can result in biases "within and across societies" @xcite , since the speakers of such languages can effectively be excluded from using language technology tools. building infrastructure that uses such technology as llms to study and preserve low-resourced languages is important. a key concern in the development of nlp infrastructure is the privacy of the data subjects and other individuals mentioned. foot_0 linguistic data typically includes names, family relationships, health status, or other sensitive details, especially when collected texts are personal conversations, narratives, or interviews @xcite , and even seemingly scarce or incomplete pi may be used to reidentify the data subject @xcite and result in discrimination based on, for example, medical conditions or faith. methods to obfuscate identities of data subjects have long been employed in linguistics @xcite , but only a few of them have used computational approaches for identification of pi in low-resourced languages like komi @xcite . since personal information has been found in data used to train llms for many high-resourced languages -raising concerns about potential leaks in their outputs @xcite -it is crucial to protect privacy of data in these languages. however, protecting personal information in low-resourced languages is especially important as these languages already struggle with limited datasets, funding, and institutional support, making them particularly vulnerable to privacy risks. in this pilot study we take a step towards better pi detection in low-resourced languages and prompt currently available llms. such models, trained on multilingual corpora, can be prompted to perform a range of tasks, from text classification to text generation, even in languages where limited training data is available @xcite . llms have been studied in the context of low-resourced uralic languages for the task of pos tagging @xcite . they have also been used to support the creation of online dictionary tools @xcite . the role of llms in pi detection in high-resourced language like english and chinese has started being explored only recently @xcite , while their role in the context of low-resourced languages for pi detection remains unexplored. to facilitate research in that direction, here we analyze the differences in the behavior of three llms in pi detection in languages with varied resource availability and linguistic structure. we use text data from two uralic languages, komi-permyak and komi-zyrian. we construct a paral-lel corpus containing komi sentences foot_1 with their polish and english translations. we prompt llama 3.1 with 8b parameters @xcite , mistral 7b @xcite , and gemma 2 with 9b parameters @xcite for pi detection and test six different prompt configurations. our contributions, therefore, consist of 1) a small, native speaker-curated parallel corpus of sentences containing potential personal information in komi, english, and polish foot_2 , and 2) an initial analysis of how three llms perform on the aforementioned dataset with respect to language's resource availability and inflectionality. ## materials and methods data we looked at the universal dependencies treebanks for komi-permyak and komi-zyrian @xcite @xcite and found that there are 366 sentences in which there is at least one word that is labeled with one of the semantic tags for proper nouns as used in the giellalt infrastructure @xcite . these semantic tags classify names and nouns into categories such as animal (sem/ani), female (sem/fem) and male names (sem/mal), objects (sem/obj), organisations (sem/org), places (sem/plc), surnames (sem/sur), and web addresses (sem/web). blokland et al. (2020) have previously used these semantic tags to identify nouns which are possible instances of pi in a rule-based pi detection system. among the sentences with semantic tags for proper nouns, 170 were translated to english and polish by authors of this study. the sentences were first translated by the first author of this study (a native komi-permyak speaker and a proficient english speaker) from komi-permyak and komi-zyrian to english with the help of neurotõlge foot_3 @xcite google translate foot_4 and majbyr translate foot_5 . polish translations were created by the second author (a native polish speaker and a proficient english speaker) based off of the english translations, and with the help of google translate in some cases. the original names of people and places were preserved during translation into english and the final form of the translated sentence was always overseen by a human. in the end, our data included 35 sentences with female names, 47 sentences with male names, 49 sentences with place names, and 39 sentences with surnames in them. some sentences contain more than one name, possibly of different types. importantly, more information that could be considered personal and which does not necessarily belong to the aforementioned types may be found in sentences, and was impossible to account for during the sentence extraction process. our resulting dataset can be accessed on zenodo via https://zenodo.org/records/14845329 . ## general error analysis after feeding the models the prompt-sentence combinations, we collected their outputs, which we subsequently manually analyzed. we begin with an analysis of the errors encountered and then proceed to examine two specific examples. in this pilot study we did not run any quantitative analysis, as the data we have lacks token-level annotation of pi in two of the three languages. moreover, the annotation that we do have for komi is using the giellalt tags, and not the aforementioned categories (1-3); thus, our analysis is preliminary. komi gemma ignores case markers in words identified as pi. for example, in the komi-zyrian sentence сiйӧ быдмис парижын, францияса юркарын 'he grew up in paris.ine foot_9 , the capital.ine of france.loc', the model marks only part of the word францияса 'france.loc', ignoring the marker -са. in contrast, it marks the entire word when it appears in the genitive case in komi-permyak, e.g. франциялӧн 'of france.gen'. when asked to tag pi, gemma often misidentifies the language as russian or urdmut and translates the text into english. it also frequently asks for more context to identify pi, refusing to produce an output. llama rarely provides output, referring to concerns about revealing information that could lead to reidentification. models are good at identifying first names and surnames (albeit worse with culture-specific names), but they struggle with names of places. for example, gemma mistakenly tags парижын 'in paris.ine' in both komi varieties as situation and франция 'france.nom' as society. llama detects spans correctly, but often assigns the wrong tag: labelling быдмис 'grew up' in both komi varieties as birth, парижын 'in paris.ine' as records, франция 'france.nom' as birth and юркарын 'the capital.ine' as society. while mistral provides output in the requested format, it struggles with tagging, changes spelling and produces many hallucinations. for example, it marks the personal pronoun сiйӧ 'he/she.nom' in komi-zyrian as pi and completely alters the initial sentence from сiйӧ быдмис парижын, францияса юркарын 'he grew up in paris.ine, the capital.ine of france.loc' to pi абыдмис пирижин, pi юркарын, where only юркарын 'the capital.ine' is a correct word. english gemma can not only mark a name as pi but also sometimes identify and tag related pronouns when asked to provide output in json format, e.g. [...] replied galina, with a dry smile from the corner of her mouth [...]. however, it does not always follow the instructions and sometimes invents tags that are not part of the tagset, such @xmath0. in one instance it is also able to assign @xmath1, where the latter is a surname and the former is a noun referring to voroshilov. llama generates extensive explanations and often refuses to perform the task, mirroring its behavior on komi. it also hallucinates tags and fails to mask multi-token pi spans accurately such as tagging only voroshilov @xmath2. while deciding whether comrade is a part of a pi span can be problematic, it is not unprecedented to find such titles included in the span: @xcite include elements like mr. or dr. into the same span as the name and surname. therefore, it is possible that inclusion of comrade in reference to voroshilov can lead to reidentifcation of this person in a different situation. mistral, while hallucinating and omitting many pi instances, performs better at masking anglophone names. for example, it correctly masks names like mary, peter, and jane using appropriate tags. however, it fails to mask names such as svezhov (ko.: свежов), petya (ko.: петя), or sasha (ko.: саша). additionally, it masks masha (ko.: маша) @xmath3, indicating a lack of understanding of the name's gender (typically female). all models demonstrate (i) a tendency to over-generate and provide unrequested explanations that are difficult to evaluate and (ii) struggle with maintaining consistency in tag assignment. polish gemma appears to misclassify inflectional cases of the words thus assigning it to the wrong gender. for example, in the sentence [...] tuż obok domu epimowa punegowa '[...] in the immediate vicinity of the epimov.gen punegov.gen house' the model mistakenly assigns epimowa and punegowa @xmath4, while both these are male names. llama refuses to perform the task stating that it cannot give away information that could lead to someone being re-identified. it also incorrectly identifies same words in same sentences across multiple prompts: under two different prompts it tags cerata 'oilcloth' as either a street name or a type of a document. mistral's output is not supplemented by extensive explanations, but the model tends to hallucinate and produce incorrect tags. for example, when asked to mark personal information as pi in dorósł w paryżu, stolicy francji 'he grew up in paris.ins, the capital of france.gen', while the model assigns pi_city to paryżu and pi_country to francji, it also incorrectly assigns pi_name to dorósł which is a verb. note that these tags are hallucinated -they are not like the ones we have prompted the model to produce. mistral also often translates polish sentences into english in its output. ## discussion and conclusions our small qualitative examination suggests that across the languages, models, and prompts that we tested, gemma with json-eliciting prompts performs best. overall, the models exhibited the best performance on the english sentences, followed by polish, with komi being the most difficult. one problem for the models is the rich morphology of komi variants and polish. the models also denote that they lack context to make a judgment, which highlights the difficulty in disambiguating whether a piece of information is personal or not. they are often trying to default to english or ask for an english translation when asked to perform the task on a low-resourced language that they cannot recognize. the models also learn differently from various tagsets: the one from @xcite is hard to generalize from, while tags based on @xcite appear to be assigned correctly more often. non-anglophone names, especially komi ones, are hard for models to tag, especially in terms of the gender. while @xcite consider their findings for high-resourced languages to be promising, we consider it better to err on the side of caution regarding any conclusions on the performance of llms on pi identification task for low-resourced languages. our impression is that even though the models' perform well on other nlp tasks, in this case, their outputs require manual post-processing and are not immune to hallucinations. this makes them highly unreliable for the incredibly highstakes task of pi detection on their own with the prompts used, even for high-resourced languages, but especially for the low-resourced ones, where the error rate appears to be higher. future work should focus on evaluating models on longer texts with more context and further refinement of the best-performing prompts. this is -to the best of our knowledge -the first study investigating the performance of llms on pi detection in more than just high-resourced languages (specifically, in such low-resourced language as komi), and the first one examining how llms handle inflectionality in this task. we are also contributing a novel parallel dataset translated by native speakers. we hope that our work will inspire more research on the topics within the intersection of llms, pi detection, and low-resourced languages.