---
title: "CoAuthor — paper by Lee et al. (2022) — summary and ideas"
description: "The authors (CHI '22) argue that the HCI community can study these generative capabilities more… 48 references, cited by 82 papers here."
authors: ["Mina Lee", "Percy Liang", "Qian Yang"]
year: 2022
venue: "arXiv:2201.06796"
source: "https://arxiv.org/abs/2201.06796"
doi: "10.1145/3491102.3502030"
arxiv: "2201.06796"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lee-2022-coauthor-designing-human-ai-collaborative/"
---
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# CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities

**Authors:** [Mina Lee](https://md.research.elamur.ai/free/en/authors/mina-lee/), [Percy Liang](https://md.research.elamur.ai/free/en/authors/percy-liang/) and [Qian Yang](https://md.research.elamur.ai/free/en/authors/qian-yang/) · **Year:** 2022 · **Venue:** [arXiv:2201.06796](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/) ([CHI Conference on Human Factors in Computing Systems 2022](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/2022/)) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1145/3491102.3502030) · [PDF](https://arxiv.org/pdf/2201.06796) · [DOI 10.1145/3491102.3502030](https://doi.org/10.1145/3491102.3502030) · [arXiv 2201.06796](https://arxiv.org/abs/2201.06796) · [OpenAlex](https://openalex.org/W4220747294)

**Citations:** 341 citations (OpenAlex, 2026-10-14) · [82](https://md.research.elamur.ai/free/en/papers/lee-2022-coauthor-designing-human-ai-collaborative/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models such as GPT-3 generate fluent text, but their usefulness to a writer varies strongly with context and is often judged subjectively. This leaves interaction designers with little solid ground. The authors (CHI '22) argue that the HCI community can study these generative capabilities more rigorously by building and mining large logs of human–model interaction, rather than relying only on interviews or informal experimentation.

To demonstrate this, they collected CoAuthor. It records people writing creative stories and argumentative essays from prompts while freely requesting, accepting, dismissing and editing GPT-3 suggestions. The authors use these logs to analyse three of the model's abilities: language (fluency), ideation (new ideas) and collaboration (working jointly with writers). They also assess GPT-3's role as a co-writer under several definitions of good collaboration. A replay tool lets researchers watch every session unfold.

The work positions interaction datasets as a shared resource between HCI and NLP. Such datasets can help generate hypotheses, test their plausibility, and train or evaluate LMs for interactive writing.

## Key points
- CoAuthor contains 1445 writing sessions by 63 writers working with four instances of GPT-3, covering creative and argumentative writing.
- Each session begins with a prompt. Writers then mix their own text with GPT-3 suggestions and may edit anything in any order, and all events are logged.
- The share of text written by the writers correlated weakly with self-reported ownership (Pearson 0.3 for both genres) and barely with satisfaction (0.1 creative, 0.0 argumentative).
- The amount of editing (approximated by delete and cursor event blocks) showed no meaningful correlation with ownership (0.1) or satisfaction (0.0 / −0.1).
- The dataset and a session replay interface are public at https://coauthor.stanford.edu.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) — University, Stanford, United States · 506 works · 330,053 citations
- [Cornell University](https://md.research.elamur.ai/free/en/universities/cornell-university/) — University, Ithaca, United States · 134 works · 65,159 citations

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## Cite

Cite as: Mina Lee, Percy Liang and Qian Yang. 2022. CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities. arXiv:2201.06796.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lee2022coauthor,
  title = {{CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities}},
  author = {Mina Lee, Percy Liang and Qian Yang},
  year = {2022},
  doi = {10.1145/3491102.3502030},
  eprint = {2201.06796},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1145/3491102.3502030}
}
```

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## References

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*Updated 2 November 2026*