The recent development of large language models (LLMs) has revolutionized the landscape of human work. These models possess the ability to follow complex human instructions and operate versatile computer software, enabling them to participate in, augment, or even automate realistic occupational tasks that once thought to be exclusive to humans. As LLMs are increasingly integrated into workplaces, they are already reshaping labor dynamics (Hoffmann et al., 2024; Demirci et al., 2025) and raising urgent concerns about job displacement, diminished human agency, and overreliance on automation (Hazra et al., 2025). As a result, the future of work is undergoing a profound transformation: How will human occupations and task requirements evolve over time? And what roles will LLM-based systems play, as they become increasingly capable collaborators and autonomous workers? And how can we build technological and data infrastructures to support human-AI collaboration? This tutorial will provide an overview of the future of work shaped by the interplay of LLMs and humans, examining the emerging challenges, opportunities, and ethical considerations in this dynamic landscape. We begin by examining the economic landscape of work and how NLP technologies drive automation, followed by methods for developing LLMs that augment human labor and recent advances in LLM-based agents. We then cover evaluation approaches for workplace contexts, including datasets, benchmarks, and metrics, and conclude with open questions on technical, human, and societal implications.

As the development of Large Language Models (LLMs) matures, the focus of the research community is undergoing a critical shift from a purely model-centric to a data-centric paradigm. It is now evident that the quality, diversity, and composition of training data—not merely its scale—are the primary drivers of a model’s advanced capabilities, from complex reasoning to reliable instruction following. However, acquiring and curating such high-quality data remains a significant bottleneck. This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. We structure the tutorial around the two core pillars of modern data strategy: intelligent data selection and advanced data synthesis. In the first part, we delve into methods for curating the most valuable information from vast, noisy datasets, covering techniques like LLM-as-a-judge for automated quality filtering and active learning for maximizing annotation efficiency. The second part explores the synthetic data revolution, detailing paradigms that range from generating complex reasoning traces (e.g., Chain-of-Thought) to deploying sophisticated multi-agent workflows that can autonomously create high-quality, diverse instruction data from raw seeds. Finally, we will conclude with a practical overview of open-source tools and platforms that facilitate these data-centric workflows, empowering researchers and practitioners to build better models through better data. Attendees will leave with a principled framework and actionable insights for designing and implementing the advanced data strategies required to build the next generation of powerful, specialized, and aligned LLMs.

Multi-agent systems powered by large language models (LLMs) offer a promising paradigm for tackling complex reasoning, decision-making, and problem-solving tasks. However, achieving both effectiveness and efficiency in such systems remains a critical challenge. This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems, focusing on three core components. First, we discuss the design of individual LLM agents. We present state-of-the-art techniques for enabling capable agents using efficient and compact LLMs, including model distillation, dynamic routing, and memory- and compute efficient serving, providing a foundation for scalable and responsive agent design under resource constraints. Second, we cover coordination and communication among agents, crucial for collective performance, highlighting methods for improving multi-agent reasoning and decision-making through prompt and graph optimization, sycophancy mitigation, and structured LLM-based frameworks. Last, we explore real-world applications of LLM agents in areas such as industry, healthcare, quantum computing, and various scientific domains.

As large language models (LLMs) increasingly tackle reasoning-heavy tasks, from mathematics to commonsense to multilingual understanding, researchers face three pressing questions: How well do models reason? How can we make them reason better? What are the next frontiers in LLM reasoning? This tutorial answers these questions through a unified view of LLM reasoning. This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of models and discusses two types of methods to improve models’ reasoning: advanced inference time methods, such as structured and self-improvement inference methods, and (ii) post-training methods, such as RLHF, DPO, and GRPO that aim to make LLMs think more like humans. The tutorial explores these technical discussions while maintaining a practical outlook through illustrative demos and short guided hands-on exercises. The tutorial is designed for both researchers and practitioners seeking practical insights into LLM reasoning.

Controlling the knowledge and behavior of generative AI systems, including large language models (LLMs), multimodal LLMs (MLLMs), and text-to-image (T2I) models, has become critical as they are increasingly used in safety-sensitive and socially impactful applications. These models often encode unintended, biased, or private content, leading to harmful or unethical outputs. Post-training knowledge control has thus emerged as a practical framework for selectively modifying or removing model behaviors without full retraining, offering scalable and interpretable interventions for improving safety, privacy, and fairness. This tutorial introduces the foundations of post-training knowledge control and showcases recent frontier methods, bridging research insights with real-world practices from both academia and industry. We cover: (i) key motivations and failure modes, such as harmful generation and stereotype reinforcement; (ii) core methods such as machine unlearning, knowledge editing, and inference-time interventions for targeted behavior adjustment; and (iii) evaluation protocols for balancing forgetting, retention, and fairness. Case studies will span text and vision–language generation, including privacy preservation, bias mitigation, and factual correction.

This tutorial will be of the type Introductory to CL/NLP topics, providing an overview of the metaphor processing field. The tutorial will discuss the influence of various metaphor theories on the creation of annotated resources and models. We will particularly focus on recent directions opened by LLMs for metaphor interpretation in multilingual and multimodal settings. Two types of audience may benefit from this tutorial: researchers in the humanities and computational social sciences interested in automatic or semi automatic metaphor analysis, and NLP researchers interested in understanding metaphor processing and improving metaphor modeling.