Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, IteraTeR is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations. Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.
CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions
Nantes Université（南特大学）；École Centrale Nantes（南特中央理工学院）；CNRS（法国国家科学研究中心）；LS2N（南特数字科学实验室）；Japanese French Laboratory for Informatics（日法信息学实验室）；NII（日本国立信息学研究所）
Text Revision by On-the-Fly Representation Optimization
The Chinese University of Hong Kong（香港中文大学）；Mila；McGill University（麦吉尔大学）；Microsoft Research Asia（微软亚洲研究院）
文本修改是指一类自然语言生成任务，其中源序列和目标序列在表面形式上具有适度的相似性，但在属性上存在差异，例如文本的正式性和简单性。目前最先进的方法将这些任务表述为序列到序列的学习问题，这依赖于大规模的并行训练语料库。在本文中，我们提出了一个迭代的就地编辑方法的文本修订，它不需要并行数据。在这种方法中，我们简单地使用掩码语言建模和属性分类来微调预训练的Transformer。在推理过程中，每次迭代的编辑都是通过两步跨度替换来实现的。在第一步，文本的分布式表示朝着属性函数动态优化。在第二步，一个文本跨度被掩盖，并提出另一个新的条件下的优化表示。对文本形式化和文本简化这两个典型的文本修改任务的实验表明了该方法的有效性。它在文本简化方面比最先进的监督方法具有竞争力甚至更好的性能，并且在文本形式化\footnote{Code and model are available at \url{https://github.com/jingjingli01/OREO}}方面比强无监督方法具有更好的性能。
Beyond Single-shot Writing: Deep Research Agents are Unreliable at Multi-turn Report Revision
New York University（纽约大学）；NYU Shanghai（上海纽约大学）；University of Alberta（阿尔伯塔大学）；Princeton University（普林斯顿大学）；University of Waterloo（滑铁卢大学）；Verdent AI, Inc.（Verdent AI公司）
Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss
Northeastern University（东北大学）；VA Connecticut Healthcare System（康涅狄格州医疗系统）；Portland Psychotherapy Clinic, Research and Training Center（波特兰心理治疗诊所、研究与培训中心）；University of Washington（华盛顿大学）；The University of Melbourne（墨尔本大学）；Drexel University（德雷塞尔大学）
Learnable Assessment Skills for LLM-based Automated Scoring: Rubric Construction via Iterative Optimization
School of Computing, University of Georgia, Athens, GA, USA（佐治亚大学计算机学院）；AI4STEM Education Center, University of Georgia, Athens, GA, USA（佐治亚大学AI4STEM教育中心）；The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）
