| 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. |
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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 . |
| Approach: | They present a review of human-model cooperation, exploring its principles, formalizations, and open challenges. |
| Outcome: | The proposed model-model cooperation paradigm has been a key focus of recent research . it is a novel paradigm that can be applied to a variety of tasks . |
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. |
| Outcome: | This tutorial will introduce scientific methods for evaluating social intelligence in LLMs, highlighting the key challenges, and identifying promising research directions. |
Future of Work in the Age of LLMs (2026.acl-tutorials)
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| 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 . |
Designing, Evaluating, and Learning from Humans Interacting with NLP Models (2023.emnlp-tutorial)
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| Challenge: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
| Approach: | They will provide a systematic overview of key considerations and effective approaches for studying human-NLP model interactions. |
| Outcome: | This tutorial will cover how to conduct human-in-the-loop usability evaluations to ensure that models are capable of interacting with humans. |
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. |
| Outcome: | The proposed game shows that humans notice and adapt to differences regardless of whether they are aware they are interacting with an LLM. |
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. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Human-AI Collaboration: How AIs Augment Human Teammates (2025.acl-tutorials)
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| Challenge: | Despite the potential of general-purpose models, they are far from perfect, excelling at certain tasks while struggling with others. |
| Approach: | This tutorial will review recent developments related to human-AI teaming and collaboration. |
| Outcome: | This tutorial will review recent developments related to human-AI teaming and collaboration. |
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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Large Human Language Models: A Need and the Challenges (2024.naacl-long)
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| Challenge: | a growing recognition of the importance of modeling human and social factors into human-centered NLP models . authors advocate for three positions toward creating large human language models based on psychological and behavioral sciences . |
| Approach: | et al. advocate for three positions toward creating large human language models . they argue that LM training should include the human context and recognize that people are more than their group . |
| Outcome: | a new study shows that learning language from linguistic signals alone is not adequate, according to a recent paper . authors advocate for three positions toward creating large human language models . a human-centered model should include the human context, and account for the dynamic nature of the human environment, they say . |
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)
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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens
| Challenge: | Recent work has sought to use large language models to simulate human-human and human-LLM interactions. |
| Approach: | They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts. |
| Outcome: | The proposed models perform similarly in simulating English, Chinese, and Russian dialogues. |