# Methods for the Design and Evaluation of HCI+NLP Systems

arXiv: https://arxiv.org/abs/2102.13461 · HTML: https://mlsanity.com/paper/2102.13461/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [Middle-Aged Video Consumers' Beliefs About Algorithmic Recommendations on YouTube](https://mlsanity.com/paper/2008.03202/) (2020)

## Built on by (most cited first, 4 of 4)

- [Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review](https://mlsanity.com/paper/2501.12557/) (2025)
- [Human-Centric Research for NLP: Towards a Definition and Guiding Questions](https://mlsanity.com/paper/2207.04447/) (2022)
- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)
- [How Much Do Large Language Models Know about Human Motion? A Case Study in 3D Avatar Control](https://mlsanity.com/paper/2505.21531/) (2025)

## Abstract (arXiv)

HCI and NLP traditionally focus on different evaluation methods. While HCI involves a small number of people directly and deeply, NLP traditionally relies on standardized benchmark evaluations that involve a larger number of people indirectly. We present five methodological proposals at the intersection of HCI and NLP and situate them in the context of ML-based NLP models. Our goal is to foster interdisciplinary collaboration and progress in both fields by emphasizing what the fields can learn from each other.