Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision (2204.03685v2)

Abstract: Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite the success of LLMs on text revision tasks, they are limited to non-iterative, one-shot revisions. Examining and evaluating the capability of LLMs for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants. In this work, we present a human-in-the-loop iterative text revision system, Read, Revise, Repeat (R3), which aims at achieving high quality text revisions with minimal human efforts by reading model-generated revisions and user feedbacks, revising documents, and repeating human-machine interactions. In R3, a text revision model provides text editing suggestions for human writers, who can accept or reject the suggested edits. The accepted edits are then incorporated into the model for the next iteration of document revision. Writers can therefore revise documents iteratively by interacting with the system and simply accepting/rejecting its suggested edits until the text revision model stops making further revisions or reaches a predefined maximum number of revisions. Empirical experiments show that R3 can generate revisions with comparable acceptance rate to human writers at early revision depths, and the human-machine interaction can get higher quality revisions with fewer iterations and edits. The collected human-model interaction dataset and system code are available at \url{https://github.com/vipulraheja/IteraTeR}. Our system demonstration is available at \url{https://youtu.be/lK08tIpEoaE}.

Human-in-the-loop Iterative Text Revision: Evaluating $\mathcal{R}$ead, $\mathcal{R}$evise, $\mathcal{R}$epeat ($\mathcal{R3$})

The paper "Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision" introduces $\mathcal{R}$ead, $\mathcal{R}$evise, $\mathcal{R}$epeat ($\mathcal{R3$), a system designed to enhance text revision processes by integrating a human-in-the-loop framework. This system is positioned as an advancement over traditional non-iterative, one-shot revision approaches, potentially enabling LLMs to better assist human writers by supporting iterative text refinement.

System Framework and Methodology

The $\mathcal{R3$ system operates by interfacing a text revision model with a human user who can iteratively accept or reject model-generated revision suggestions. This human interaction is a pivotal component of $\mathcal{R3$, facilitating the filtering of noise and enhancing the quality of the revisions. The system is trained using an extensive revision dataset across diverse domains, employing models like RoBERTa and Pegasus for edit intention identification and text revisions, respectively.

Empirical Evaluation and Results

The researchers conducted a comprehensive empirical paper comparing Human-Human, System-Human, and System-Only revision processes. Notably, the System-Human approach—which encapsulates the core human-in-the-loop strategy—demonstrated revision efficacy comparable to human-only revisions, particularly at the initial depths. The acceptance rates for revisions made using $\mathcal{R3$ were found to be in proximity to those made by humans, with certain edit types, such as style edits, achieving higher acceptance.

Discussion and Implications

The findings indicate that $\mathcal{R3$ holds potential for reducing cognitive load on human writers by streamlining the revision process with minimal human effort. However, the performance decreases at deeper iteration levels, suggesting areas for future improvement in model training and interactive design. The system advances the theoretical understanding of iterative revision dynamics and opens pathways for further exploration into the optimization of collaborative writing interfaces.

Future Directions

Potential enhancements to the $\mathcal{R3$ system include refining edit intention models to improve edit quality, particularly for less accurate intentions such as fluency. Additionally, expanding user interactions beyond simple accept/reject prompts could yield deeper insights into user preferences and model adaptability. As AI continues to evolve, systems like $\mathcal{R3$ exemplify the push towards more nuanced and human-centric AI applications, suggesting broader implications for educational tools, professional writing aids, and beyond.

Overall, the introduction of $\mathcal{R3$ signifies an important step in integrating machine intelligence with human creativity in the writing process. This research lays a foundational framework for subsequent developments in human-computer interactive text revision, championing a future where AI assists rather than controls the creative process.