Human-Centric Research for NLP: Towards a Definition and Guiding Questions
Not on the map. Why?
Abstract (arXiv)
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes research human-centric? We address this question by providing a working definition and define how a research pipeline can be split into different stages in which human-centric components can be added. Additionally, we discuss existing NLP with HCR components and define a series of guiding questions, which can serve as starting points for researchers interested in exploring human-centric research approaches. We hope that this work would inspire researchers to refine the proposed definition and to pose other questions that might be meaningful for achieving HCR.
Key facts. Cited by 9 papers in the map (data through 13 Oct 2026). Builds on 32 of its 65 references that are in the map. First posted on arXiv 10 Jul 2022.
Builds on
32 of 65 references.
References · 32
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,880
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,729
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,410
- 2018Datasheets for Datasetscited by 1,377
- 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 998
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 992
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 810
- 2015Evaluating the visualization of what a Deep Neural Network has learnedcited by 417
- 2016Rationalizing Neural Predictionscited by 281
- 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
- 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 252
- 2021A Survey of Human-in-the-loop for Machine Learningcited by 214
20 more
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 183
- 2021Expanding Explainability: Towards Social Transparency in AI systemscited by 124
- 2020STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generationcited by 56
- 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
- 2022Rethinking Explainability as a Dialogue: A Practitioner's Perspectivecited by 52
- 2018Neural Open Information Extractioncited by 50
- 2021Putting Humans in the Natural Language Processing Loop: A Surveycited by 40
- 2016Rationale-Augmented Convolutional Neural Networks for Text Classificationcited by 39
- 2020How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labelscited by 39
- 2020Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifierscited by 35
- 2020IMoJIE: Iterative Memory-Based Joint Open Information Extractioncited by 34
- 2019OPIEC: An Open Information Extraction Corpuscited by 20
- 2020If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skillscited by 20
- 2019SemEval-2019 Task 8: Fact Checking in Community Question Answering Forumscited by 18
- 2020Weakly- and Semi-supervised Evidence Extractioncited by 16
- 2020What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Designcited by 14
- 2021Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Modelscited by 12
- 2021Decision-Focused Summarizationcited by 11
- 2021Personalized Benchmarking with the Ludwig Benchmarking Toolkitcited by 6
- 2021Methods for the Design and Evaluation of HCI+NLP Systemscited by 4
Built on by
Citing papers per year: 2023: 2; 2024: 4; 2025: 3.
9 papers.
- 2024Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agentscited by 73
- 2023Aligning Language Models with Human Preferences via a Bayesian Approachcited by 27
- 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
- 2023Linking Surface Facts to Large-Scale Knowledge Graphscited by 5
- 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarcecited by 4
- 2025On Synthesizing Data for Context Attribution in Question Answeringcited by 2
- 2024Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language modelscited by 0
- 2024Human-Centric NLP or AI-Centric Illusion?: A Critical Investigationcited by 0
- 2025TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Actioncited by 0
- 2025TextMineX: Data, Evaluation Framework and Ontology-guided LLM Pipeline for Humanitarian Mine Action18 Sep 2025
- 2025On Synthesizing Data for Context Attribution in Question Answering21 Feb 2025
- 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions19 Feb 2025
- 2024Human-Centric NLP or AI-Centric Illusion?: A Critical Investigation14 Dec 2024
- 2024Building Better: Avoiding Pitfalls in Developing Language Resources when Data is Scarce16 Oct 2024
- 2024Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language models26 Jul 2024
- 2024Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents14 Feb 2024
- 2023Linking Surface Facts to Large-Scale Knowledge Graphs23 Oct 2023
- 2023Aligning Language Models with Human Preferences via a Bayesian Approach9 Oct 2023