Large Language Models (LLMs) often struggle with creative generation, and multi-agent frameworks that improve reasoning through interaction can paradoxically hinder creativity by inducing content homogenization. We introduce LLM Review, a peer-review-inspired framework implementing Blind Peer Review: agents exchange targeted feedback while revising independently, preserving divergent creative trajectories. To enable rigorous evaluation, we propose SciFi-100, a science fiction writing dataset with a unified framework combining LLM-as-a-judge scoring, human annotation, and rule-based novelty metrics. Experiments demonstrate that LLM Review consistently outperforms multi-agent baselines, and smaller models with our framework can surpass larger single-agent models, suggesting interaction structure may substitute for model scale.
Pron vs Prompt: Can Large Language Models already Challenge a World-Class Fiction Author at Creative Text Writing?
School of Computer Science, UNED, Madrid, Spain（计算机科学学院，UNED，马德里，西班牙）；Faculty of Education, UCM, Madrid, Spain（教育学院，UCM，马德里，西班牙）；Faculty of Philosophy, UNED, Madrid, Spain（哲学学院，UNED，马德里，西班牙）
Exposía: Teaching and Assessment of Academic Writing Skills for Research Project Proposals and Peer Feedback
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt（普遍知识处理实验室（UKP实验室）、计算机科学系和海斯曼人工智能中心（hessian.AI）、达姆施塔特技术大学）；Department of Computer Science at Queens College, City University of New York (CUNY)（纽约城市大学皇后学院计算机科学系）
Large Language Models Penetration in Scholarly Writing and Peer Review
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Electronic Science and Technology of China（电子科技大学）；Wuhan University of Technology（武汉理工大学）；University of Copenhagen（哥本哈根大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
The Geometry of Creative Variability: How Credal Sets Expose Calibration Gaps in Language Models
Department of Statistics, LMU Munich（统计系，慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍兹研究中心）
Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations
Graduate Institute of Communication Engineering, National Taiwan University（台湾大学通信工程研究院）；Computer Science Department, University of California, Los Angeles（加州大学洛杉矶分校计算机科学系）；Graduate Institute of Networking and Multimedia, National Taiwan University（台湾大学网络与多媒体研究院）；Department of Electrical Engineering, National Taiwan University（台湾大学电子工程系）
Controllable Narrative Rendering for Enhanced Assisted Writing
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；HiThink Research（海天瑞声）
Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs' generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3's capabilities in assisting creative and argumentative writing. CoAuthor captures rich inter…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Adobe Research（Adobe研究院）
While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored. We leverage advances in language modeling to build an interactive writing assistant that generates and rephrases text according to fine-grained author specifications. Users provide input to our Intent-Guided Assistant (IGA) in the form of text interspersed with tags that correspond to specific rhetorical directives (e.g., adding description or contrast, or rephrasing a particular sentence). We…
Recent developments in natural language generation (NLG) using neural language models have brought us closer than ever to the goal of building AI-powered creative writing tools. However, most prior work on human-AI collaboration in the creative writing domain has evaluated new systems with amateur writers, typically in contrived user studies of limited scope. In this work, we commissioned 13 professional, published writers from a diverse set of creative writing backgrounds to craft stories using Wordcraft, a text editor with built-in AI-powered writing assistance tools. Using interviews and pa…
