Improving Occupational ISCO Classification of Multilingual S wiss Job Postings with LLM -Refined Training Data (2025)

Abstract: Classifying occupations in multilingual job postings is challenging due to noisy labels, language variation, and domain-specific terminology. We present a method that refines silver-standard ISCO labels by consolidating them with predictions from pre-fine-tuned models, using large language model (LLM) evaluations to resolve discrepancies. The refined labels are used in Multiple Negatives Ranking (MNR) training for SentenceBERT-based classification. This approach substantially improves performance, raising Top-1 accuracy on silver data from 37.2% to 58.3% and reaching up to 80% precision on held-out data—an over 30-point gain validated by both GPT and human raters. The model benefits from cross-lingual transfer, with particularly strong gains in French and Italian. These results demonstrate hat LLM-guided label refinement can substantially improve multilingual occupation classification in fine-grained taxonomies such as CH-ISCO with 670 classes.

## introduction a job is "a set of tasks and duties performed, or meant to be performed, by one person", and an occupation is "a set of jobs whose main tasks and duties are characterized by a high degree of similarity" @xcite . the job title in job postings often reveals the occupation (e.g., "tax advisor"), but vague titles (e.g., "associate") require additional context. educational requirements shape how job ads convey occupations, from broad (e.g., "a humanities degree") to specific, regulated qualifications (e.g., "grafikerin efz" (graphic designer with swiss federal diploma), "rechtsanwalt" (lawyer with bar admission). in switzerland's educationfocused market, these formal qualifications often map directly to distinct roles. some ads allow multiple backgrounds, such as " @xcite polymechaniker" (precision, micro, or polymechanic), underscoring how tasks and qualifications together define an occupation. our dataset of 4.7 million swiss job ads shows 80% are german, 11% french, 8% english, and under 1% italian. many postings intermix these languages, especially in job titles, and commercial machine translation services often left terms untranslated (e.g., "compliance officer"), or translated them to unusual german terms. classification must thus handle these multilingual code-switching phenomena. occupation is vital in labor market research, informing most comparative studies and statistical analyses of job ads. our aim is to extract occupation-relevant content from these ads and map it to a system suited to the swiss labor market, while preserving international comparability. ch-isco-19 (hereafter ch-isco) is a swiss adaptation of the international standard classification of occupations (isco) that organizes roles into five levels with 670 detailed classes. while it aligns with isco at the first four levels, the fifth level extends coverage for switzerland's labor market. official labels in german, french, and italian exist at all levels, but english coverage remains limited, especially for finer distinctions. this structure ensures both swiss-specific granularity and international compatibility, making ch-isco a natural target for classifying job ads in switzerland. to address the noise in existing labeled data, complex role descriptions, and multilingual content, our approach combines multilingual embedding adaptation with structured ontologies and uses llms to refine silver-standard training data. we first learn semantic similarities between in-domain texts and occupation labels via masked language modeling (mlm) and mnr, optimized for german ch-isco classes but supporting multiple languages. llm-based validation then consolidates high-confidence examples into a cleaner dataset. this pipeline preserves swiss-specific detail and isco compatibility while addressing the multilingual complexity of job ads. this paper makes the following key contributions: (1) we demonstrate an llm-assisted approach for rating isco occupation candidates, exploring prompt variations and in-context examples. ## related work most occupation classification pipelines rely on job titles, offering limited robustness for noisy job ads. traditional systems often emphasize structured taxonomies such as isco (international labour organization, 2012), yet typically overlook issues of label misalignment or noise @xcite . we build on ch-isco (swiss federal statistical office (fso), 2022), augmenting its taxonomy with multilingual embeddings to better handle real-world classification challenges. recent advances in large language models (llms) have expanded annotation capabilities: gpt-style models can generate synthetic data @xcite or re-rank classification candidates @xcite . we follow this trend by using gpt-based assessments to refine the "silver-standard" sjmm labels @xcite , specifically focusing on rating occupation candidates through in-context examples. this distinguishes our method from purely synthetic data approaches by leveraging existing-albeit noisy-labels and merging them with llm-evaluated consistency checks. for multilingual model updates, sentence-level embeddings derived from bert-style encoders @xcite have proven effective in information retrieval tasks. however, they often require domain adaptation @xcite , particularly in specialized labor market contexts. researchers have also introduced knowledgedistillation schemes to extend pretrained models across languages with fewer resources @xcite . building on such efforts, we propose a domainadapted, multilingual sentencebert (sbert) that retains ch-isco alignment while addressing jobad diversity and language imbalance. we then incorporate an llm-based curation step, which filters and reweights training data, thus mitigating label noise and enhancing multilingual performance. the fso maps over 23k swiss occupations to ch-isco, providing around 45k job titles in german, 41k in french, 39k in italian, and 6.5k in english. fewer english titles partly stem from its gender-neutral usage versus other languages' malefemale distinctions. for newer it or managerial roles (e.g., "devops engineer"), english names are often adopted across languages. however, english remains underrepresented, prompting us to focus on german labels while continuing to support multilingual input in the classification pipeline. esco (european commission, 2017) extends coverage by providing around 15.5k german and french titles, 16k italian titles, and 32k english titles, plus english descriptions for all 1-4-digit isco classes. the swiss job market monitor (sjmm) 3 provides for the period 1990-2023 approximately 65k job ads with 5-digit ch-isco labels. however, these labels were derived from the older manual sbn2000 classifications (swiss federal statistical office (fso), 2017) rather than being directly annotated according to ch-isco. unlike the skill-based ch-isco, sbn2000 groups occupations by economic fields, e.g., classifying doctors and nurses together under healthcare, whereas ch-isco separates them by skill requirements. since no direct crosswalk exists, mappings rely on occupational titles, often assigned with limited precision, focusing more on selecting a fitting sbn2000 class than ensuring an exact match. this conversion introduces misalignment that is difficult to quantify. despite this limitation, the data remains a valuable "silver standard" for training and evaluation. the ch-isco class distribution in the suf data is highly diverse: 498 of 670 possible classes (74.3%) are represented, with a relative entropy of 81% @xcite . this broad spread highlights the complexity of the classification task. ## domain-specific sbert models multilingual occupation classification requires models that handle job-specific terminology while supporting both german and multilingual inputs. german is key, as most ontological and job ad data are in this language, but multilingual capability multilingual anchors [bjt] kommunikationsplanerin [ejt] [bjt] communications planner [ejt] [bjt] planificateur en communication [ejt] [bjt] pianificatrice di comunicazione [ejt] you analyse and plan the way a brand is positioned on the market. sie analysieren und planen die positionierung einer marke auf dem markt. ils analysent et planifient la mise en place d'une marque sur le marché. analizzano e pianificano le modalità di immissione sul mercato di un marchio. [bjt] medienmanager [ejt] positive [bjt] fachkräfte in marketing und werbung [ejt] we reuse the german jobgbert @xcite foot_3 , further pre-trained on 2 million spans from job ads, including task descriptions, work activities, and translated o*net foot_4 occupation class titles. we refer to this model as joblmde. for multilingual adaptation, we continue mlm training xlm-robertabase @xcite using the transformers library @xcite on a balanced dataset of 7.8 million job ads, upsampling less frequent languages to ensure robust cross-lingual performance. training follows best practices from @xcite . 6 the resulting domain-adapted multilingual model is referred to as joblmmulti. we focus on these models as prior experiments showed that sbert fine-tuning on domain-adapted language models consistently outperforms fine-tuning of general-purpose sbert variants. we fine-tune both models using the sentence-bert library @xcite with mnr loss @xcite to align job titles with their corresponding isco classes, simulating the classification task. given our multilingual focus, we train on job titles and descriptions (ch-isco and esco data) in four languages, linking them to german ch-isco labels. we also conducted mnr adaptation experiments using cross-lingual translation pairs from the available ontological data. while this approach substantially increased the amount of training data, it did not yield improvements on the target classification task. ## llm-assisted data curation approach this approach targets the limitations of silverstandard sjmm labels, which-due to conversion issues-introduce noise that affects both training and evaluation. by leveraging llm-based validation, we identify and resolve label discrepancies to enhance the reliability of training data. ## ch-isco-specific mnr training data curation: results and discussion the preceding feasibility study confirms that gpt ratings offer a reliable way to evaluate ch-isco class candidates, showing strong internal consistency and reasonable alignment with sjmm and sbert predictions. however, disagreements-both among human annotators and between gpt and human ratings-underscore the need for careful curation when incorporating gpt- to generate ch-isco-specific mnr training data, we select cases where at least two sources-gpt, sbert, or sjmm-agree. job ad titles and descriptions serve as anchors, with agreed classes as positives. negative examples are drawn primarily from gpt-rated 0.0 candidates, with random sampling used as a fallback. to ensure effectiveness, we apply weighted sampling to amplify the impact of high-confidence classifications. classes rated 1.0 by gpt receive a weight of 2, reflecting strong confidence, while those rated 0.5 receive a weight of 1. if the sjmm silver label matches sbert's top candidate, it is included as a positive example with weight 1-reinforcing agreement without overemphasis. ## conclusion and future work we show that llm-assisted data curation leads to marked improvements in sbert-based multilingual occupation classification. the refinement process consolidates information from two sources-the original silver-standard labels in the sjmm data and predictions from a pre-fine-tuned sbert model-using gpt ratings to resolve discrepancies and validate candidate assignments. fine-tuning sbert with this gpt-refined data substantially improves alignment with the sjmm silver standard, raising top-1 accuracy from 37.2% to 58.3%. more importantly, on held-out random test data, we achieve an average top-1 precision of 80%-up from around 50% before fine-tuning-as confirmed by both gpt and human raters. this marks a significant gain in a fine-grained (670class) and complex classification task. while task-specific fine-tuning reduces top-1 accuracy on the original ontology-based classification task by approximately 10 points, top-3 accuracy remains high at 95%. the impact is limited and acceptable, given that ontology retention is not the primary objective. in our case, gpt's consistency and structured scoring provide a reliable mechanism for curating high-quality training data. the results suggest that a single round of gpt-based refinement is both effective and efficient-delivering strong improvements in classification accuracy.