# Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level

arXiv: https://arxiv.org/abs/2311.07434 · HTML: https://mlsanity.com/paper/2311.07434/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2023.

## Built on by (most cited first, 26 of 26)

- [CollabLLM: From Passive Responders to Active Collaborators](https://mlsanity.com/paper/2502.00640/) (2025)
- [(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice](https://mlsanity.com/paper/2402.01864/) (2024)
- [Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies](https://mlsanity.com/paper/2502.08554/) (2025)
- [CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions](https://mlsanity.com/paper/2508.01674/) (2025)
- [IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2504.16728/) (2025)
- [Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses](https://mlsanity.com/paper/2312.00763/) (2023)
- [Exploring Prompt Engineering Practices in the Enterprise](https://mlsanity.com/paper/2403.08950/) (2024)
- [Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset](https://mlsanity.com/paper/2503.23899/) (2025)
- [OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models](https://mlsanity.com/paper/2508.21061/) (2025)
- [PrefIx: Understand and Adapt to User Preference in Human-Agent Interaction](https://mlsanity.com/paper/2602.06714/) (2026)
- [LLM Content Moderation and User Satisfaction: Evidence from Response Refusals in Chatbot Arena](https://mlsanity.com/paper/2501.03266/) (2025)
- [DiscoverLLM: From Executing Intents to Discovering Them](https://mlsanity.com/paper/2602.03429/) (2026)
- [Beyond Passive Critical Thinking: Fostering Proactive Questioning to Enhance Human-AI Collaboration](https://mlsanity.com/paper/2507.23407/) (2025)
- [Large Language Models Meet User Interfaces: The Case of Provisioning Feedback](https://mlsanity.com/paper/2404.11072/) (2024)
- [Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks](https://mlsanity.com/paper/2410.24032/) (2024)
- [Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models](https://mlsanity.com/paper/2606.10949/) (2026)
- [Surprisingly Fragile: Assessing and Addressing Prompt Instability in Multimodal Foundation Models](https://mlsanity.com/paper/2408.14595/) (2024)
- [AISA: Awakening Intrinsic Safety Awareness in Large Language Models against Jailbreak Attacks](https://mlsanity.com/paper/2602.13547/) (2026)
- [Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01158/) (2024)
- [Enhancing Patient-Centric Communication: Leveraging LLMs to Simulate Patient Perspectives](https://mlsanity.com/paper/2501.06964/) (2025)
- [Steerable Chatbots: Exploring Personalization Control Interfaces via LLM Activation Steering](https://mlsanity.com/paper/2505.04260/) (2025)
- [Voice CMS: updating the knowledge base of a digital assistant through conversation](https://mlsanity.com/paper/2505.22303/) (2025)
- [Let Them Down Easy! Contextual Effects of LLM Guardrails on User Perceptions and Preferences](https://mlsanity.com/paper/2506.00195/) (2025)
- [AwesomeLit: Towards Hypothesis Generation with Agent-Supported Literature Research](https://mlsanity.com/paper/2603.22648/) (2026)
- [Prism: Towards Lowering User Cognitive Load in LLMs via Complex Intent Understanding](https://mlsanity.com/paper/2601.08653/) (2026)
- [U-Define: Designing User Workflows for Hard and Soft Constraints in LLM-Based Planning](https://mlsanity.com/paper/2605.02765/) (2026)

## Abstract (arXiv)

Large language models (LLMs) with chat-based capabilities, such as ChatGPT, are widely used in various workflows. However, due to a limited understanding of these large-scale models, users struggle to use this technology and experience different kinds of dissatisfaction. Researchers have introduced several methods, such as prompt engineering, to improve model responses. However, they focus on enhancing the model's performance in specific tasks, and little has been investigated on how to deal with the user dissatisfaction resulting from the model's responses. Therefore, with ChatGPT as the case study, we examine users' dissatisfaction along with their strategies to address the dissatisfaction. After organizing users' dissatisfaction with LLM into seven categories based on a literature review, we collected 511 instances of dissatisfactory ChatGPT responses from 107 users and their detailed recollections of dissatisfactory experiences, which we released as a publicly accessible dataset. Our analysis reveals that users most frequently experience dissatisfaction when ChatGPT fails to grasp their intentions, while they rate the severity of dissatisfaction related to accuracy the highest. We also identified four tactics users employ to address their dissatisfaction and their effectiveness. We found that users often do not use any tactics to address their dissatisfaction, and even when using tactics, 72% of dissatisfaction remained unresolved. Moreover, we found that users with low knowledge of LLMs tend to face more dissatisfaction on accuracy while they often put minimal effort in addressing dissatisfaction. Based on these findings, we propose design implications for minimizing user dissatisfaction and enhancing the usability of chat-based LLM.