Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 5: Tutorial Abstracts)
Creative Planning with Language Models: Practice, Evaluation and Applications (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | This tutorial explores how planning has been learned and deployed in creative workflows . many human creative tasks involve extensive planning, and actions need to be taken . |
| Approach: | This tutorial explores how planning has been learned and deployed in creative workflows . authors discuss forward and backward learning approaches for planning in LLMs - and evaluation metrics tailored to latent plans . |
| Outcome: | This tutorial examines how planning has been learned and deployed in creative workflows . it discusses forward and backward learning approaches for planning in LLMs - evaluation metrics tailored to latent plans . |
DAMAGeR: Deploying Automatic and Manual Approaches to GenAI Red-teaming (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | In this tutorial, we will review and apply current automatic and manual red-teaming techniques for GenAI models. |
| Approach: | This tutorial will review automatic and manual red-teaming techniques for GenAI models . |
| Outcome: | This tutorial will review and apply current automatic and manual red-teaming techniques for GenAI models. |
Foundation Models Meet Embodied Agents (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents . |
| Approach: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents. |
| Outcome: | This tutorial covers three types of foundation models for embodied agents . |
Knowledge Distillation for Language Models (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | Knowledge distillation (KD) aims to transfer knowledge from a teacher to a student . this tutorial will cover topics ranging from LLM sequence compression to LLM self-distillation . |
| Approach: | They propose to introduce intermediate-layer matching and prediction matching . they will then present advanced techniques such as reinforcement learning-based KD and multi-teacher distillation . |
| Outcome: | This tutorial aims to provide participants with a comprehensive understanding of the techniques and applications of knowledge distillation for language models. |
Adaptation of Large Language Models (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities. |
| Approach: | This tutorial will provide an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
| Outcome: | This tutorial will outline dynamic, domain-specific, and task-adaptive LLM adaptation techniques. |
Learning Language through Grounding (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | This tutorial provides a historical overview of grounding and discusses its use in computational linguistics and in computational language processing. |
| Approach: | They introduce the concept of grounding and discuss future directions and open challenges . they will delve into recent progress in learning lexical semantics, syntax, and complex meanings through various forms of ground. |
| Outcome: | This course will provide an overview of the field of grounding and discuss future directions and challenges related to large language models and scaling. |
LLMs and Copyright Risks: Benchmarks and Mitigation Approaches (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have revolutionized natural language processing, but their widespread use has raised significant copyright concerns. |
| Approach: | This tutorial will provide an overview of relevant copyright principles and their application to AI and examine specific copyright issues in LLM development and deployment. |
| Outcome: | The course will provide an overview of relevant copyright principles and their application to AI, followed by an examination of specific copyright issues in LLM development and deployment. |
Social Intelligence in the Age of LLMs (2025.naacl-tutorial)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are a powerful tool for integrating human-like communication and context-aware interactions into artificial systems. |
| Approach: | They propose to introduce and overview different aspects of artificial social intelligence and their relationship with LLMs by introducing scientific methods for evaluating social intelligence in LLM. |
| Outcome: | This tutorial will introduce scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. |