# Task Supportive and Personalized Human-Large Language Model Interaction: A User Study

arXiv: https://arxiv.org/abs/2402.06170 · HTML: https://mlsanity.com/paper/2402.06170/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2024.

## Built on by (most cited first, 17 of 17)

- [AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy](https://mlsanity.com/paper/2402.07862/) (2024)
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- [Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design](https://mlsanity.com/paper/2505.00134/) (2025)
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- [Learning from AVA: Early Lessons from a Curated and Trustworthy Generative AI for Policy and Development Research](https://mlsanity.com/paper/2604.17843/) (2026)
- [Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild](https://mlsanity.com/paper/2605.05767/) (2026)
- [GOLF: Goal-Oriented Long-term liFe tasks supported by human-AI collaboration](https://mlsanity.com/paper/2403.17089/) (2024)
- [Personalized Image Generation from an Author Writing Style](https://mlsanity.com/paper/2507.03313/) (2025)
- [Survey for Categorising Explainable AI Studies Using Data Analysis Task Frameworks](https://mlsanity.com/paper/2507.10208/) (2025)
- [Chrysalis: A Unified System for Comparing Active Teaching and Passive Learning with AI Agents in Education](https://mlsanity.com/paper/2510.05271/) (2025)
- [Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making](https://mlsanity.com/paper/2602.11924/) (2026)
- [Learning Transferable Latent User Preferences for Human-Aligned Decision Making](https://mlsanity.com/paper/2605.12682/) (2026)
- [ASSEMCAD: Production-Ready CAD Assembly Generation from Natural Language](https://mlsanity.com/paper/2607.05123/) (2026)

## Abstract (arXiv)

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.