# Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management

arXiv: https://arxiv.org/abs/2507.13275 · HTML: https://mlsanity.com/paper/2507.13275/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://mlsanity.com/paper/2402.16829/) (2024)
- [JobBERT: Understanding Job Titles through Skills](https://mlsanity.com/paper/2109.09605/) (2021)
- [Linq-Embed-Mistral Technical Report](https://mlsanity.com/paper/2412.03223/) (2024)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers](https://mlsanity.com/paper/2109.06501/) (2021)
- [Skill Extraction from Job Postings using Weak Supervision](https://mlsanity.com/paper/2209.08071/) (2022)
- [Learning Job Titles Similarity from Noisy Skill Labels](https://mlsanity.com/paper/2207.00494/) (2022)
- [Overview of BioASQ 2022: The tenth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2210.06852/) (2022)
- [MELO: An Evaluation Benchmark for Multilingual Entity Linking of Occupations](https://mlsanity.com/paper/2410.08319/) (2024)
- [R'esum'e Parsing as Hierarchical Sequence Labeling: An Empirical Study](https://mlsanity.com/paper/2309.07015/) (2023)
- [Qwen2.5-32B: Leveraging Self-Consistent Tool-Integrated Reasoning for Bengali Mathematical Olympiad Problem Solving](https://mlsanity.com/paper/2411.05934/) (2024)

## Built on by (most cited first, 3 of 3)

- [Multilingual JobBERT for Cross-Lingual Job Title Matching](https://mlsanity.com/paper/2507.21609/) (2025)
- [Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2606.31692/) (2026)
- [TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2607.20009/) (2026)

## Abstract (arXiv)

Advances in natural language processing and large language models are driving a major transformation in Human Capital Management, with a growing interest in building smart systems based on language technologies for talent acquisition, upskilling strategies, and workforce planning. However, the adoption and progress of these technologies critically depend on the development of reliable and fair models, properly evaluated on public data and open benchmarks, which have so far been unavailable in this domain. To address this gap, we present TalentCLEF 2025, the first evaluation campaign focused on skill and job title intelligence. The lab consists of two tasks: Task A - Multilingual Job Title Matching, covering English, Spanish, German, and Chinese; and Task B - Job Title-Based Skill Prediction, in English. Both corpora were built from real job applications, carefully anonymized, and manually annotated to reflect the complexity and diversity of real-world labor market data, including linguistic variability and gender-marked expressions. The evaluations included monolingual and cross-lingual scenarios and covered the evaluation of gender bias. TalentCLEF attracted 76 registered teams with more than 280 submissions. Most systems relied on information retrieval techniques built with multilingual encoder-based models fine-tuned with contrastive learning, and several of them incorporated large language models for data augmentation or re-ranking. The results show that the training strategies have a larger effect than the size of the model alone. TalentCLEF provides the first public benchmark in this field and encourages the development of robust, fair, and transferable language technologies for the labor market.