Matching job titles is a highly relevant task in the computational job market domain, as it improves e.g., automatic candidate matching, career path prediction, and job market analysis. Furthermore, aligning job titles to job skills can be considered an extension to this task, with similar relevance for the same downstream tasks. In this report, we outline NLPnorth's submission to TalentCLEF 2025, which includes both of these tasks: Multilingual Job Title Matching, and Job Title-Based Skill Prediction. For both tasks we compare (fine-tuned) classification-based, (fine-tuned) contrastive-based, and prompting methods. We observe that for Task A, our prompting approach performs best with an average of 0.492 mean average precision (MAP) on test data, averaged over English, Spanish, and German. For Task B, we obtain an MAP of 0.290 on test data with our fine-tuned classification-based approach. Additionally, we made use of extra data by pulling all the language-specific titles and corresponding descriptions from ESCO for each job and skill. Overall, we find that the largest multilingual language models perform best for both tasks. Per the provisional results and only counting the unique teams, the ranking on Task A is 5th/20 and for Task B 3rd/14.
ments [
identifying current workforce needs, emerging job roles, and skill shortages. Existing methods for skill extraction range from traditional rule-based and pattern-matching approaches to advanced machine learning and deep learning techniques, using both supervised and unsupervised methods [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25].
skills to other similar skills or to existing taxonomies such as ESCO [26]. This research area frequently explores innovative methodologies, such as leveraging self-supervised learning techniques, transformerbased language models, and semantic embeddings for skill representation and matching [27, 28, 29, 30, 17, 31, 32, 33, 34]. Similarly, job title classification and matching to, e.g., SOC [ 35] or ISCO [36] have been explored extensively, addressing challenges related to standardizing diverse job titles, which vary significantly across organizations and regions. Techniques include supervised classification, semantic matching algorithms, and transformer-based models such as JobBERT [37], which facilitate matching and categorization of job titles [38, 39, 40, 19, 41, 42]. These skills and job titles are also being further classified into their respective taxonomical counterparts to be used for further labor market demand analysis [43, 44].
future career trajectories, facilitating career counseling and workforce planning [45, 46]. Lastly, significant attention has been devoted to mapping jobs and skills onto standardized occupational and skill taxonomies, aiding systematic labor market research and policy-making decisions [47, 31, 48, 49, 50].
matching (Task B). Both tasks require mapping free-form labour-market text onto a structured knowledge base, yet they difer in language coverage, label granularity, and training-data volume. We show example annotations for both tasks in Table 1. We systematically compare three paradigms that dominate current
a multilingual encoder with cross-entropy loss. (2) Contrastive learning, which learns task-specific sentence embeddings via InfoNCE and relies on cosine ranking at inference. (3) Prompting, which directly exploits instruction-tuned LLM embedders in a zero-shot setting, requiring no task-specific updates.
the contrastive models, we additionally exploit corresponding ESCO descriptions. Our experimental results show that contrastive learning excels on multilingual title matching, whereas discriminative ifne-tuning leads on job–skill prediction. Surprisingly, zero-shot prompting with a 7B parameter model performs close to supervised systems on Task A, highlighting the rapid progress of instruction-tuned
Contributions: • We present the first side-by-side comparison of discriminative, contrastive, and prompting paradigms for both job–title and job–skill matching. • We introduce a unified ESCO-derived training corpus (titles, alternative labels, and multilingual descriptions) and release preprocessing scripts to facilitate future research.1 • We achieve 5th place out of 20 teams on Task A and 3rd out of 14 on Task B by carefully tuning model size, negative-sampling strategy, and inference prompts, demonstrating that model choice should be task-specific rather than one-size-fits-all.
This task challenges participants to build multilingual systems that, given a job title, rank related titles from a knowledge base [51]. Provided resources include: • Training Data: 15,000 labeled pairs of related job titles per language (English, Spanish, German) support cross-lingual training. • Development Data: Participants receive 100 manually annotated samples per language, each with a query job title and related titles. A language-specific knowledge base is also provided for ranking. This data also includes Chinese. • Test Data: Participants receive 5,000 job titles, with evaluation based on a gold-standard subset of 100 titles per language. Titles include hidden annotations for industry and gender, allowing bias assessment alongside ranking performance. This data also includes Chinese.
This task challenges participants to build models that, given a job title, return related skills from a knowledge base. All data is in English.
• Training Data: 2,000 job titles with associated professional skills, sourced from real job descriptions and semi-automatically curated for accuracy. • Development Data: 200 job titles with related skills normalized to ESCO terminology. A skills knowledge base is also provided. • Test Data: 500 job titles for which participants must predict related skills using the provided knowledge base.
positive instance consists of a query title and one related title. We create negative instances by pairing the same query with randomly sampled, unrelated titles. We experiment with positive–to–negative ratios of 1:1, 1:2, and 1:5 and select the best ratio (1:2) on the English development data. At prediction time, we use the output of the softmax to create a ranking (as opposed to using the binary labels directly).
tilingual encoder and optimizes cross-entropy loss. We fine-tune several multilingual models given their domain representativeness and high performance on other classification tasks: escoxlm-r [53], m-e5-large-instruct [54], m-e5-large [54], mdeberta-v3-base [55], and par-m-mpnet-base-v2 [56].
learning rate of 0.0001, batch size of 32, 20 epochs, and a slanted-triangular learning rate scheduler [57].
titles with related skills. Negatives are sampled from the entire skill vocabulary, the optimal negative ratio for taskB was 1:1.
Training data. We construct pairs from ESCO. For each occupation we create (i) preferred title → description and (ii) preferred title → alternative title. Negatives come from the remaining descriptions or titles.
aligned pairs (, ), we treat every other ( ̸= ) as a hard negative for and vice versa: ℓ = − log
exp(sim(, )) ∑︀=1 exp(sim(, )) (1) We use cosine similarity for sim(· ). The overall loss is the average of the ℓ.
{1, 2, 5, 10, 15, 16, 20, 32}, batch size ∈ {16, 32, 64}, and learning rate ∈ {1 × 10− 4, 5 × 10− 5, 2 × 10− 5, 2 × 10− 6}. The optimal configuration uses = 16, batch size 32, and learning rate 2 × 10− 6. ∈ Adapting to Task B. We build three pair types: (i) job → skill, (ii) job → alt_skill, and (iii) alt_job → skill. We sample negatives from the complementary pool (all skills or all jobs, respectively).
bed text with multilingual-e5-large-instruct (560M), Linq-Embed-Mistral (7.11B) and gte-Qwen2-7B-instruct (7.61B). In this setup, we embed the query with a task description prefix, and we embed the candidate jobs/skills separately. Then, we rank candidates by cosine similarity. We use the following prefixes:
Task A: “Given a job title, find the most relevant job titles.”
Task B: “Given a job title, find the most relevant skills.”
prompting on the test data, but their results are quite close on bath datasplits. It should be noted that the prompting model is 14 times larger, but did not require fine-tuning. The classification based models perform worse. The trends across diferent language models are consistent, larger models perform better, and m-e5-large provides a strong performance across approaches (except for prompting, due
to its size). Performance on the languages also shows a highly similar trend. English gets the best performance, followed by Chinese, Spanish, and German. This is somewhat surprising, as Chinese is more distant to the other languages, and also completely unseen during training. We also note that the performance on the test data is lower compared to the validation data for all models. This could be a sign of overfitting, but after inspecting the data, we also saw that there is a larger overlap of job titles (~20% versus ~0%) with the train data. We hope details about the data creation process can shed more light on these diferences.
Table 3 breaks the scores down by language pair for the prompt-based models (which are the only one we submitted for the crosslingual track). gte-Qwen2-7B-instruct achieves the strongest
performance (0.461). In-language results show the same trend: It tops English (0.537) and Spanish (0.496), while remaining competitive on German (0.442). These findings suggest that (i) prompting benefits most from the larger pre-training signal in English and Spanish ESCO descriptions, and (ii) cosine-similarity scoring is robust across both monolingual and cross-lingual retrieval scenarios.
outperform the other models on both the validation and the test data. Overall, the MAP scores are also much lower compared to task A, showing that the mapping of skills to jobs is a more challenging task.
models perform best for Task A. We take the three best-performing models in each method category (i.e., classification, contrastive, prompting) from Task A and map each data point from the validation set to their respective ESCO major group. In Table 5, we report the amount of job titles which can be mapped to a specific ESCO code. For English, there are 77.4% unmapped titles and for both Spanish and
in categories such as “managers”, “professionals”, “technicians and associate professionals”, “clerical support workers”, and “service and sales workers” are the most dificult to predict. In contrast, we see job titles from categories such as “armed forces occupations”, “craft and related trades”, “plant and machine operators” and “elementary occupations” being often predicted correctly. In the case of unmapped job titles, we see that most models do not perform well.
prompting is efective for multilingual job title matching (Task A) and classification approaches excel in predicting job-related skills (Task B). However for task B, prompting-based methods, despite their lower performance, show promising results in a zero-shot scenario, suggesting potential avenues for further exploration.
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