Papers with editor
Content-Based Conflict of Interest Detection on Wikipedia (L18-1)
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| Challenge: | Conflict-of-Interest (CoI) editing is a problem on Wikipedia that is highly subjective . a key feature of Wiki sites is to allow people from all over the world to add or modify articles anonymously and without consequence. |
| Approach: | They frame CoI detection as a binary classification problem and explore features for it . they find that stylometric features outperform other types of features and give an F-measure of 0.63 . |
| Outcome: | The proposed method outperforms other features and gives an F-measure of 0.63 . the proposed method is not certain that the set of non-CoI articles contains any CoI articles . |
Langsmith: An Interactive Academic Text Revision System (2020.emnlp-demos)
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| Challenge: | Currently, diversity and inclusion initiatives in the academic community are encouraged . however, writing papers in English can be a daunting task . |
| Approach: | They propose a system that helps non-native English speakers to write papers in English . the system can suggest fluent, academic-style sentences based on their rough, incomplete phrases or sentences . |
| Outcome: | The proposed system can help non-native English speakers write papers in English . the system can suggest fluent, academic-style sentences based on their rough sentences . |
RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor (2023.emnlp-demo)
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Khanh Duy Nguyen, Zixuan Zhang, Reece Suchocki, Sha Li, Martha Palmer, Susan Windisch Brown, Jiawei Han, Heng Ji
| Challenge: | Existing IE tools for atomic events are limited when applied to such complex events. |
| Approach: | They propose to use event schemas to guide the organization of complex events and to edit hierarchical graphs. |
| Outcome: | The proposed tool outperforms existing IE visualization tools in both IE result analysis and general model improvements. |
Personalized Abstractive Summarization by Tri-agent Generation Pipeline (2024.findings-eacl)
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| Challenge: | Existing research shows that large language models do not consistently satisfy users' preferences or expectations. |
| Approach: | They propose a tri-agent generation pipeline that includes a generator, an instructor, and an editor to enhance output personalization. |
| Outcome: | The proposed pipeline generates outputs that better meet user expectations on two abstractive summarization datasets. |
Tools for Building an Interlinked Synonym Lexicon Network (L18-1)
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| Challenge: | a new lexicon is being developed for cross-lingual (Czech and English) synonyms based on their syntactic and semantic behavior in (bilingual) context. |
| Approach: | They propose to build a new interlinked verbal synonym lexicon called CzEngClass using a tool that helps to keep cross-lingual synonym classes consistent. |
| Outcome: | The proposed lexicon captures cross-lingual (Czech and English) synonyms . the tool, called Synonym Class Editor -SynEd, is customized to build and edit entries . |
Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection (2026.acl-long)
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| Challenge: | Existing methods to detect large language models (LLMs) use binary or ternary classifications, which can only distinguish pure human/LLM text or collaborative text at best. |
| Approach: | They propose a fine-grained method that characterizes distinct signatures of creator and editor by using Rhetorical Structure Theory to construct a logic graph for creator's foundation and extracting Elementary Discourse Unit (EDU)-level features for the editor's style. |
| Outcome: | The proposed method outperforms 12 baselines in identifying fine-grained types with low false alarms, offering a policy-aligned solution for LLM regulation. |
Interactive Text Generation (2023.emnlp-main)
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Felix Faltings, Michel Galley, Kianté Brantley, Baolin Peng, Weixin Cai, Yizhe Zhang, Jianfeng Gao, Bill Dolan
| Challenge: | Advances in generative modeling have made it possible to automatically generate high-quality texts, code, and images, but they can be unsatisfactory in many respects. |
| Approach: | They propose a task that allows training generation models interactively without the costs of involving real users. |
| Outcome: | The proposed model trains with Imitation Learning without the cost of involving real users and is superior to non-interactive models. |
JRE-L: Journalist, Reader, and Editor LLMs in the Loop for Science Journalism for the General Audience (2025.naacl-long)
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| Challenge: | Science journalism reports current scientific discoveries to non-specialists, aiming to enable public comprehension of the state of the art. |
| Approach: | They propose a framework that integrates three LLMs mimicking the writing-reading-feedback-revision loop. |
| Outcome: | The proposed framework generates articles that are more accessible than existing methods, including prompting single advanced models such as GPT-4 and other LLM-collaboration strategies. |
Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers (2023.findings-emnlp)
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| Challenge: | Recent applications of LLMs in Machine Reading Comprehension systems have shown impressive results, but the use of shortcuts has emerged as a potential threat to their reliability. |
| Approach: | They propose a framework that guides an editor to add potential shortcuts-triggers to samples. |
| Outcome: | The proposed framework can edit trigger shortcuts in samples that fool LLMs . it also shows that GPT4 can be deceived by its own edits (15% drop in F1). |
Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Large language models (LLMs) improve event synthesis, but most are monolithic and often process overlapping evidence with bursty reporting patterns. |
| Approach: | They propose a multi-agent framework that casts TLS as a *newsroom-like* collaboration. |
| Outcome: | Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency. |