Challenge: a new framework to describe request-making segments user input into request content, roles assigned, query-specific context, and task-independent expressions.
Approach: They propose a framework to describe request-making that segments user input into request content, roles assigned, query-specific context, and the remaining task-independent expressions.
Outcome: The proposed framework reveals fundamental and habitual user-LLM interaction patterns beyond individual task completion.

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Challenge: a recent study shows that asking for direct user feedback can be disruptive . we examine whether incorporating the contents of user feedback improves model performance .
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Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild (2025.emnlp-main)

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Challenge: Rather than passively accepting output, users actively refine, explore, and co-construct text.
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Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)

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Challenge: Recent work has sought to use large language models to simulate human-human and human-LLM interactions.
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WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026.acl-long)

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Challenge: Traditional alignment methods rely on human annotations and are subjective and misalignment with real-world user preferences.
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The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? (2024.emnlp-main)

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Challenge: Large language models have shown capabilities close to human performance in various analytical tasks.
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The Death and Life of Great Prompts: Analyzing the Evolution of LLM Prompts from the Structural Perspective (2024.emnlp-main)

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Challenge: Recent research has shown that high-quality prompts are essential for LLMs to produce accurate and relevant responses.
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Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)

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Challenge: Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
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Navigating Rifts in Human-LLM Grounding: Study and Benchmark (2025.acl-long)

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Challenge: Language models excel at following instructions but struggle with collaborative aspects of conversation that humans naturally employ.
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The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)

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Challenge: Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks.
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Label and Explanation Variation in LLM-Based Annotation: a Case Study in Natural Language Inference (2026.acl-long)

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Challenge: Large language models (LLMs) have shown considerable promise for annotation purposes, but questions remain about their ability to capture human label variation (HLV) label variation is genuine disagreement between annotators observed across NLP tasks.
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