EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text editing have been proposed for various types of data with diverse attributes. |
| Approach: | They propose a novel text editing method that modifies reference text to desired attributes at various scales. |
| Outcome: | The proposed method is capable of making precise adjustments within the desired range while maintaining the accuracy of the reference text. |
Similar Papers
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that large language models can perform precise text editing tasks. |
| Approach: | InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models . |
| Outcome: | The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits. |
A Non-Autoregressive Edit-Based Approach to Controllable Text Simplification (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models that generate generic simplified outputs for a given source text have been used to specify output properties. |
| Approach: | They propose a non-autoregressive model that iteratively edits an input sequence and incorporates lexical complexity information into the refinement process to generate simplifications that better match the desired output complexity. |
| Outcome: | The proposed model incorporates lexical complexity information into the refinement process to achieve more complex simplification operations such as content deletion and paraphrasing, as well as sentence splitting. |
Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for autoregressive text generation have low controllability and accumulating errors. |
| Approach: | They propose a three-stage prompt-based approach to express autoregressive text in a specific region editing task using a word frequency-based strategy. |
| Outcome: | Experiments on publicly competitive datasets confirm that the proposed approach achieves state-of-the-art performance. |
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework (2024.emnlp-main)
Copied to clipboard
| Challenge: | RSA-Control is a training-free controllable text generation framework . existing studies rely on fine-tuning pre-trained language models . external components could hurt coherence and accuracy of the model . |
| Approach: | They propose a training-free controllable text generation framework grounded in pragmatics that directs the generation process by recursively reasoning between imaginary speakers and listeners. |
| Outcome: | The proposed framework achieves strong attribute control while maintaining fluency and content consistency. |
ControlText: Unlocking Controllable Fonts in Multilingual Text Rendering without Font Annotations (2025.findings-emnlp)
Copied to clipboard
Bowen Jiang, Yuan Yuan, Xinyi Bai, Zhuoqun Hao, Alyson Yin, Yaojie Hu, Wenyu Liao, Lyle Ungar, Camillo Jose Taylor
| Challenge: | a new method for visual text rendering requires glyph annotations to be obtained . |
| Approach: | They propose a model that integrates diffusion with a text segmentation model to achieve multilingual text rendering using just raw images without font label annotations. |
| Outcome: | The proposed model can achieve font-controllable multilingual text rendering without label annotations. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
Copied to clipboard
Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
Editing Large Language Models: Problems, Methods, and Opportunities (2023.emnlp-main)
Copied to clipboard
Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
| Challenge: | Recent advances in model editing for LLMs have created challenges and opportunities for the community. |
| Approach: | They propose to alter the behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
| Outcome: | The proposed method alters behavior of LLMs efficiently within a specific domain without negatively impacting performance across other inputs. |
Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA (2023.emnlp-main)
Copied to clipboard
| Challenge: | Traditional human evaluation methods for text simplification often relies on individual, shallow sentence-level ratings, easily affected by the annotator's preference or bias. |
| Approach: | They propose an edit-based human annotation framework that enables holistic and fine-grained text simplification evaluation. |
| Outcome: | The proposed framework is able to predict sentence- and word-level quality simultaneously and report promising results. |
Text Generation with Text-Editing Models (2022.naacl-tutorials)
Copied to clipboard
Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, Aliaksei Severyn
| Challenge: | Text-editing models are a popular alternative to seq2seq for monolingual text generation tasks such as text summarization and style transfer. |
| Approach: | They propose to use text-editing models to predict edit operations applied to the source sequence and to generate outputs word-by-word from scratch. |
| Outcome: | This paper provides an overview of the text-edit based models and their current state-of-the-art approaches. |
REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)
Copied to clipboard
Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan
| Challenge: | Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate. |
| Approach: | They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector. |
| Outcome: | The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE. |