mlsanityData to 7 Oct 2026

Parachute: Evaluating Interactive Human-LM Co-writing Systems

arXiv 2303.06333 ↗First posted 11 Mar 2023cs.HC cs.AI cs.CLcited by 7

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Abstract (arXiv)

A surge of advances in language models (LMs) has led to significant interest in using LMs to build co-writing systems, in which humans and LMs interactively contribute to a shared writing artifact. However, there is a lack of studies assessing co-writing systems in interactive settings. We propose a human-centered evaluation framework, Parachute, for interactive co-writing systems. Parachute showcases an integrative view of interaction evaluation, where each evaluation aspect consists of categorized practical metrics. Furthermore, we present Parachute with a use case to demonstrate how to evaluate and compare co-writing systems using Parachute.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 11 Mar 2023.

Builds on

9 of 22 references.

References · 9

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  4. 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
  5. 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 97
  6. 2022Evaluating Human-Language Model Interactioncited by 90
  7. 2022Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systemscited by 59
  8. 2020STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generationcited by 56
  9. 2022Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summariescited by 47

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Citing papers per year: 2023: 5; 2024: 1; 2025: 1.

7 papers.