| Challenge: | a tutorial examines the future of work shaped by the interplay of large language models and humans . a series of tutorials examines challenges, opportunities, and ethical considerations in this dynamic landscape . |
| Approach: | This tutorial examines the future of work shaped by the interplay of LLMs and humans . it examines how LLM-based systems can augment human labor and enhance real-world tasks . |
| Outcome: | This tutorial examines the future of work shaped by the interplay of LLMs and humans . it examines challenges, opportunities, and ethical considerations in this dynamic landscape . |
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| Challenge: | Large Language Models (LLMs) have revolutionized the capabilities of AI systems. |
| Approach: | This tutorial will provide an overview of the interaction between humans and Large Language Models (LLMs) it will start with a review of the types of AI models we interact with and walkthrough of the core concepts in Human-AI Interaction. |
| Outcome: | This tutorial will provide an overview of the interaction between humans and LLMs, exploring the challenges, opportunities, and ethical considerations that arise in this dynamic landscape. |
Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)
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Vinod Muthusamy, Yara Rizk, Kiran Kate, Praveen Venkateswaran, Vatche Isahagian, Ashu Gulati, Parijat Dube
| Challenge: | Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs. |
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How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond (2025.acl-long)
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| Challenge: | Using large language models, intelligent models have evolved into autonomous agents . this paradigm has yielded remarkable progress in numerous NLP tasks in recent years . |
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How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
| Approach: | They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge. |
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Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)
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| Challenge: | general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data. |
| Approach: | This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks . |
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Human Alignment: How Much Do We Adapt to LLMs? (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) are becoming a common part of our lives, yet few studies have examined how they influence our behavior. |
| Approach: | They propose a cooperative language game in which players aim to converge on a word and play a game in a group. |
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Social Intelligence in the Age of LLMs (2025.naacl-tutorial)
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| 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. |
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Adaptation of Large Language Models (2025.naacl-tutorial)
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| 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. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
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| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents. |
| Approach: | They introduce a foundational three-level taxonomy to delineate their escalating autonomy and evolving responsibilities within the research lifecycle. |
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