# CARE: Collaborative AI-Assisted Reading Environment

arXiv: https://arxiv.org/abs/2302.12611 · HTML: https://mlsanity.com/paper/2302.12611/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review](https://mlsanity.com/paper/2204.10805/) (2022)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [UKP-SQUARE: An Online Platform for Question Answering Research](https://mlsanity.com/paper/2203.13693/) (2022)
- [TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation](https://mlsanity.com/paper/2208.07846/) (2022)

## Built on by (most cited first, 7 of 7)

- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [PeerQA: A Scientific Question Answering Dataset from Peer Reviews](https://mlsanity.com/paper/2502.13668/) (2025)
- [Large Language Models Penetration in Scholarly Writing and Peer Review](https://mlsanity.com/paper/2502.11193/) (2025)
- [SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts](https://mlsanity.com/paper/2604.26506/) (2026)
- ['Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic Assumptions](https://mlsanity.com/paper/2606.04906/) (2026)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)
- [Expos'ia: Teaching and Assessment of Academic Writing Skills for Research Project Proposals and Peer Feedback](https://mlsanity.com/paper/2601.06536/) (2026)

## Abstract (arXiv)

Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking. We propose inline commentary as a natural vehicle for AI-based reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading. CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering. The extensible behavioral logging allows unique insights into the reading and commenting behavior, and flexible configuration makes the platform easy to deploy in new scenarios. To evaluate CARE in action, we apply the platform in a user study dedicated to scholarly peer review. CARE facilitates the data collection and study of inline commentary in NLP, extrinsic evaluation of NLP assistance, and application prototyping. We invite the community to explore and build upon the open source implementation of CARE.