# Taxonomy of User Needs and Actions

arXiv: https://arxiv.org/abs/2510.06124 · HTML: https://mlsanity.com/paper/2510.06124/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 202 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 30 of 34)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Co-Writing with Opinionated Language Models Affects Users' Views](https://mlsanity.com/paper/2302.00560/) (2023)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://mlsanity.com/paper/2505.13995/) (2025)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [The Ethics of Advanced AI Assistants](https://mlsanity.com/paper/2404.16244/) (2024)
- [Evaluating Large Language Models on Controlled Generation Tasks](https://mlsanity.com/paper/2310.14542/) (2023)
- [Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers](https://mlsanity.com/paper/2504.18412/) (2025)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks](https://mlsanity.com/paper/2305.11430/) (2023)
- [Grounding Gaps in Language Model Generations](https://mlsanity.com/paper/2311.09144/) (2023)
- [The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing](https://mlsanity.com/paper/2403.12004/) (2024)
- [Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies](https://mlsanity.com/paper/2309.13063/) (2023)
- [What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Use](https://mlsanity.com/paper/2409.08775/) (2024)
- [MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations](https://mlsanity.com/paper/1908.10023/) (2019)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification](https://mlsanity.com/paper/2101.07124/) (2021)
- [Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs](https://mlsanity.com/paper/2505.05660/) (2025)
- [Capturing Humans' Mental Models of AI: An Item Response Theory Approach](https://mlsanity.com/paper/2305.09064/) (2023)
- ["I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI](https://mlsanity.com/paper/2410.08526/) (2024)
- [Comprehension Based Question Answering using Bloom's Taxonomy](https://mlsanity.com/paper/2106.04653/) (2021)

## Built on by (most cited first, 15 of 15)

- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Measuring and Mitigating the Distributional Gap Between Real and Simulated User Behaviors](https://mlsanity.com/paper/2605.07847/) (2026)
- [Cultural Compass: A Framework for Organizing Societal Norms to Detect Violations in Human-AI Conversations](https://mlsanity.com/paper/2601.07973/) (2026)
- [A Unified Framework to Quantify Cultural Intelligence of AI](https://mlsanity.com/paper/2603.01211/) (2026)
- [Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversations](https://mlsanity.com/paper/2605.02624/) (2026)
- [Cultural Authenticity: Comparing LLM Cultural Representations to Native Human Expectations](https://mlsanity.com/paper/2604.03493/) (2026)
- [Evaluation of Agents under Simulated AI Marketplace Dynamics](https://mlsanity.com/paper/2604.14256/) (2026)
- ["I Didn't Make the Micro Decisions": Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration](https://mlsanity.com/paper/2605.21363/) (2026)
- [Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond](https://mlsanity.com/paper/2606.18062/) (2026)
- [Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States](https://mlsanity.com/paper/2609.15972/) (2026)
- [Pro-AI Bias in Large Language Models](https://mlsanity.com/paper/2601.13749/) (2026)
- [DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering](https://mlsanity.com/paper/2604.15140/) (2026)
- [From Information to Delegation: Mapping Human-AI Financial Decision Making](https://mlsanity.com/paper/2608.02100/) (2026)
- [CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships](https://mlsanity.com/paper/2609.00250/) (2026)
- [Dataset Signatures in Human-LLM Interactions and User Modeling](https://mlsanity.com/paper/2610.05534/) (2026)

## Abstract (arXiv)

The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices through which they achieve them. Existing taxonomies of conversational behavior either overgeneralize, remain domain-specific, or reduce interactions to narrow dialogue functions. To address this gap, we introduce the Taxonomy of User Needs and Actions (TUNA), an empirically grounded framework developed through iterative qualitative analysis of 1193 human-AI conversations, supplemented by theoretical review and validation across diverse contexts. TUNA organizes user actions into a three-level hierarchy encompassing behaviors associated with information seeking, synthesis, procedural guidance, content creation, social interaction, and meta-conversation. By centering user agency and appropriation practices, TUNA enables multi-scale evaluation, supports policy harmonization across products, and provides a backbone for layering domain-specific taxonomies. This work contributes a systematic vocabulary for describing AI use, advancing both scholarly understanding and practical design of safer, more responsive, and more accountable conversational systems.