mlsanityData to 14 Oct 2026

Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications

arXiv 2306.16143 ↗First posted 28 Jun 2023cs.CL cs.AI cs.HCcited by 5

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Abstract (arXiv)

User experience (UX) is a part of human-computer interaction (HCI) research and focuses on increasing intuitiveness, transparency, simplicity, and trust for the system users. Most UX research for machine learning (ML) or natural language processing (NLP) focuses on a data-driven methodology. It engages domain users mainly for usability evaluation. Moreover, more typical UX methods tailor the systems towards user usability, unlike learning about the user needs first. This paper proposes a new methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating system usefulness and user utility. The methodology emerged from and is evaluated on a case study about the full-cycle prototype development of a domain-specific semantic search for daily operations in the process industry. A key finding of our case study is that involving domain experts increases their interest and trust in the final NLP application. The combined UX+NLP research of the proposed method efficiently considers data- and user-driven opportunities and constraints, which can be crucial for developing NLP applications.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 72 references that are in the map. First posted on arXiv 28 Jun 2023.

Builds on

18 of 72 references.

References · 18

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6 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Languagecited by 3
  2. 2026PaperTrail: A Claim-Evidence Interface for Grounding Provenance in LLM-based Scholarly Q&Acited by 2
  3. 2025Link Prediction for Event Logs in the Process Industrycited by 1
  4. 2026An Expert Schema for Evaluating Large Language Model Errors in Scholarly Question-Answering Systemscited by 1
  5. 2025Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industrycited by 0