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.

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 \emph{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 5$^{\text{th}}$/20 and for Task B 3$^{\text{rd}}$/14.

Weaknesses: My first concern is that this model seems far from minimalism. Generating correct answer for RPM is an interesting task. But one of the reasons it is interesting to the current AI community is that humans can somehow generate some results correctly without huge amount of training. Although this work demonstrates the possibility of generator that can show some reasoning capability, I highly speculate that this is a distillation from the subnetworks for context extraction, which is trained with strong supervision. There is still a long distance from this model and human brain. The latter one is believed to be designed by nature following minimalism.

The domain of human resources (HR) includes a broad spectrum of tasks related to natural language processing (NLP) techniques. Recent breakthroughs in NLP have generated significant interest in its industrial applications in this domain and potentially alleviate challenges such as the difficulty of resource acquisition and the complexity of problems. At the same time, the HR domain can also present unique challenges that drive state-of-the-art in NLP research. To support this, we provide NLP researchers and practitioners with an overview of key HR tasks from an NLP perspective, illustrating how specific sub-tasks (e.g., skill extraction) contribute to broader objectives (e.g., job matching). Through this survey, we identify opportunities in NLP for HR and suggest directions for future exploration.

If you follow business technology trends, you've likely heard that in the future artificial intelligence will play a role in almost every aspect of business operations -- from sales and marketing to the customer experience. While AI is not yet mainstream, it is gaining traction among many businesses. People use it more than they realize, and there are possibilities across every industry. Once only available to the largest, most financially sound corporations, AI and machine learning are finding their way into the small businesses that make up the backbone of the United States economy. They are reshaping the way firms conduct business, allowing owners to do more with less.

Brazil, India, China, and South Africa are the only countries where more than half of the population expressed strong trust and acceptance of artificial intelligence technologies, according to a study from global accounting firm KPMG. The country with the highest trust in A.I. is India, with a 75% overall acceptance rate. Moreover, the study revealed that emerging countries --specifically the BRICS bloc-- also have the highest engagement with A.I. China is the nation with the most people using A.I. in their workplace (75%), followed by India with 66% and Brazil with 50%. On the other hand, citizens of developed countries appeared to be more skeptical.

Human Resources (HR) departments play a critical role in managing an organization's most valuable asset -- its people. From recruiting new talent to managing employee benefits and compensation, HR teams are responsible for ensuring a company's workforce is engaged, productive, and motivated. HR departments can now leverage AI tools like ChatGPT to streamline their processes and achieve greater efficiency. ChatGPT can be a powerful tool for HR professionals in a variety of ways, including automating repetitive tasks, providing real-time support to employees, and enhancing the overall employee experience. Let's dive into some specific use cases for ChatGPT in human resources and talk about the benefits these types of language models can bring to HR departments and organizations as a whole.

Organizations are increasingly exploring opportunities to use artificial intelligence (AI) to manage talent more effectively, fairly, and efficiently. However, the use of AI in Human Resources (HR) has come under scrutiny because of multiple concerns, such as data privacy and bias. Further, since over 250 different commercial AI-based HR tools exist, this landscape can be challenging to navigate. This newly released 59-page toolkit developed in collaboration with a community of over 50 experts provides ideas to promote the responsible use of AI-based tools in HR. This resource contains an overview of AI in HR, how AI works, and critical considerations for the responsible adoption and monitoring of AI systems. It includes two editable checklists and questionnaires to guide the evaluation and implementation of HR-based AI platforms.

The future of work will place complex demands on already overwhelmed human resources teams.

Artificial Intelligence (AI) systems have been increasingly used to make decision-making processes faster, more accurate, and more efficient. However, such systems are also at constant risk of being attacked. While the majority of attacks targeting AI-based applications aim to manipulate classifiers or training data and alter the output of an AI model, recently proposed Sponge Attacks against AI models aim to impede the classifier's execution by consuming substantial resources. In this work, we propose \textit{Dual Denial of Decision (DDoD) attacks against collaborative Human-AI teams}. We discuss how such attacks aim to deplete \textit{both computational and human} resources, and significantly impair decision-making capabilities. We describe DDoD on human and computational resources and present potential risk scenarios in a series of exemplary domains.