The use of large language models (LLMs) in human-centered creative domains — such as journalism, scientific writing, and storytelling — has showcased their potential for content generation but highlighted a critical gap: planning. Planning, a fundamental process in many creative domains, refers to higher level decisions writers (or agents) make that influence textual output they produce. Planning is especially hard to perform in creative domains, where human rewards are often unclear or sparsely observed. This tutorial explores how planning has been learned and deployed in creative workflows, unifying three scenarios: Full Data Regimens (when observational data for decisions and resulting text exist), Partial (when text exists but decisions can be inferred) and Low (when neither exist). The tutorial discusses forward and backward learning approaches for planning in LLMs, evaluation metrics tailored to latent plans, and practical applications in computational journalism, web agents, and other creative domains. By bridging theoretical concepts and practical demonstrations, this tutorial aims to inspire new research directions in leveraging LLMs for creative and goal-oriented planning tasks.

T1: Creative Planning with Language Models: Practice, Evaluation and Applications

In this tutorial, we will review and apply current automatic and manual red-teaming techniques for GenAI models (including LLMs and multimodal models). We will also iteratively engage tutorial participants in live red-teaming activities to collaboratively learn impactful red-teaming strategies and share insights. **[Read more...](https://sites.google.com/view/naacl25red-teaming-tutorial/)**

T2: DAMAGeR: Deploying Automatic and Manual Approaches to GenAIRed-teaming

An embodied agent is a generalist agent that can take natural language instructions from humans and perform a wide range of tasks in diverse environments. Recent years have witnessed the emergence of Large Language Models as powerful tools for building Large Agent Models, which have shown remarkable success in supporting embodied agents for different abilities such as goal interpretation, subgoal decomposition, action sequencing, and transition modeling (causal transitions from preconditions to post-effects).

However, moving from Foundation Models to Embodied Agents poses significant challenges in understanding lower-level visual details, and long-horizon reasoning for reliable embodied decision-making. We will cover the advances of the foundation models into Large Language Models Vision-Language Models, and Vision-Language-Action Models. In this tutorial, we will comprehensively review existing paradigms for foundations for embodied agents, and focus on their different formulations based on the fundamental mathematical framework of robot learning, Markov Decision Process (MDP), and present a structured view to investigate the robot’s decision-making process.

Please find more info at https://foundation-models-meet-embodied-agents.github.io .

T3: Foundation Models Meet Embodied Agents

Knowledge distillation (KD) aims to transfer the knowledge of a large model (usually referred to as the teacher) to a small one (usually referred to as the student). In this tutorial, our goal is to provide participants with a comprehensive understanding of the techniques and applications of KD for language models. For the techniques, we will present the challenge of the multi-modality issue and how to mitigate it, whereas for applications, we will emphasize large language models including KD for prompting systems and human preference alignment. The target audience is expected to know the basics of machine learning and NLP, but not have to be familiar with the details of math derivation and neural models. **[Read more...](https://manga-uofa.github.io/llm-kd/)**

T4: Knowledge Distillation for Language Models

This tutorial on adaptation of Large Language Models (LLMs) is designed to address the growing demand for models that go beyond the static capabilities of generic LLMs by providing an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques. While general LLMs have demonstrated strong generalization across a variety of tasks, they often struggle to perform well in specialized domains such as finance, healthcare, and code generation for underrepresented languages. Additionally, their static nature limits their ability to evolve with the changing world, and they are often extremely large in size, making them impractical and costly to deploy at scale. As a result, the adaptation of LLMs has drawn much attention since the birth of LLMs and is of core importance, both for industry, which focuses on serving its targeted users, and academia, which can greatly benefit from small but powerful LLMs. 

To address this gap, this tutorial aims to provide an overview of the LLM adaptation techniques. We start with an introduction to LLM adaptation, from both the data perspective, which has been widely accepted as one of the most important ingredients in LLM training, and the model perspective, which focuses on the training strategies to adapt the LLMs. We then emphasize how the evaluation metrics and benchmarks are different from other techniques. After establishing the problems in the aforementioned sessions, we explore various adaptation techniques. We categorize adaptation techniques into two main families. The first is parametric knowledge adaptation, which focuses on updating the parametric knowledge within LLMs, including methods like Domain-Adaptive Pre-Training (DAPT), Instructional Tuning (IT), and Preference Learning (PL) via human or model feedback. Additionally, we will discuss real-time adaptation techniques, including model editing, which allows LLMs to be updated dynamically in production environments. The second kind of adaptation is semi-parametric knowledge adaptation, where the goal is to update LLM parameters to better leverage external knowledge or tools (e.g., documents or functions) through techniques like retrieval-augmented generation (RAG) and agent-based systems. 

Please find more info at https://adapt-llm.github.io/.

T5: Adaptation of Large Language Models

Grounding has been a long-standing concept in natural language processing (NLP) and computational linguistics (CL). This tutorial provides a historical overview and introduces recent advances in learning language through grounding, with a particular emphasis on the latter. We will begin by tracing the history of grounding and presenting a unified perspective on the term. In Parts II to IV, we will delve into recent progress in learning lexical semantics, syntax, and complex meanings through various forms of grounding. We will conclude by discussing future directions and open challenges, particularly those related to the growing trend of large language models and scaling. **[Read more...](https://language-grounding.github.io/)**

T6: Learning Language through Grounding

The rise of Large Language Models (LLMs) like GPT has significantly reshaped the field of artificial intelligence. These models have transformed natural language processing and extended their impact across sectors such as healthcare, software development, finance, and education.

While LLMs have enabled remarkable advancements in text generation and analysis, they have also introduced complex legal and ethical concerns—particularly in relation to copyright. Their ability to generate human-like content raises difficult questions about originality and ownership, with recent legal disputes, including a case brought by The New York Times against Microsoft, highlighting these tensions.

This tutorial offers a clear and in-depth overview of the copyright challenges posed by LLMs. It is designed to help participants understand the evolving legal landscape and develop strategies for navigating these emerging issues. **[Read more...](https://github.com/liamjxu/NoticeBench)**

T7: LLMs and Copyright Risks: Benchmarks and Mitigation Approaches

With the emergence of Large Language Models (LLMs), we now have unprecedented opportunities to incorporate human-like communication and context-aware interactions into artificial systems. But what is the current state of LLMs' capability of social interaction? Can they truly understand social scenarios, perform social reasoning, or interact with humans as socially competent agents? We propose this tutorial as an introduction to and an overview of different aspects of artificial social intelligence and their relationship with LLMs. 

In this tutorial, we will explore these questions by introducing scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. Participants will not only gain a comprehensive overview of the field’s progress, but also acquire technical skills on analysing and developing LLM-based social intelligence. **[Read more...](https://naacl2025.opensocial.world)**

T8: Social Intelligence in the Age of LLMs

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

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NAACL 2025 Tutorials Program

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics


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