Text Generation

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33 papers in the last four weeks, up 175% on the four weeks before. 0.3% of all new papers.

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The Significance of Style Diversity in Annotation-Free Synthetic Data Generation
Jun 18, 2026Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam +1Synthetic Data Generation Controllable Text Generation
University of Amsterdam, Amsterdam, The Netherlands · Booking.com, Amsterdam, The Netherlands
CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges
Jun 18, 2026Helena Bonaldi, Genoveffa Martone, Marco GueriniContent Moderation Text Generation
Fondazione Bruno Kessler, Italy · Università Cattolica del Sacro Cuore, Italy
ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
Jun 18, 2026Zhibang Yang, Xinke Jiang, Yuzhen Xiao +9Long-Form Document Generation Deep Research Agents
National Engineering Research Center of Software Engineering, Peking University, Beijing, China · School of Computer Science, Peking University, Beijing, China · Key Laboratory of High Confidence Software Technologies, Ministry of Education, Beijing, China +2
Source-Grounded Data Generation for Text-to-JSON Learning
Jun 18, 2026Sunghee Ahn, Guijin Son, Youngjae YuGrounded Text Generation Synthetic Data Generation
1Seoul National University
A Variational Framework for LLM Generator-Regulator Games
Jun 16, 2026Quanyan ZhuLLM Safety Text Generation
Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, USA.
NarrativeWorldBench: A Frontier-Saturated Benchmark and a Latent World Model for Long-Horizon Co-Creative Audio Drama
Jun 16, 2026Logan Mann, Abdur Rahman, Mohammad Saifullah +2LLM World Models Multilingual Language Model Evaluation
University of California, Santa Barbara · Pocket FM
Do Large Language Models Always Tell The Same Stories?
Jun 15, 2026Thennal DK, Hans Ole HatzelLLM Evaluation Diverse Text Generation
Language Technology Group University of Hamburg
Symbolic Informalization: Fluent, Productive, Multilingual
Jun 15, 2026Aarne RantaText Generation
Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg
Creative Collision: Directorial Persona Steering and Competition in Large Language Models
Jun 15, 2026Subramanyam Sahoo, Justin ShenkLanguage Model Steering Personality Modeling in Language Models
Horizon Research.
Interactor: Agentic RL oriented Iterative Creation for Ad Description Generation in Sponsored Search
Jun 14, 2026Penghui Wei, Jiayu Wu, Chao Ye +3Online Advertising RL for Generative Models
Search Ads, Baidu Inc.
Agentic Retrieval and Reinforcement Learned Equation Chains: A Controlled Generation Framework for Complex and Novel Physics Word Problems
Jun 14, 2026Tirthankar MittraGenerative AI in Education Text Generation
TriAdReview: Triangular Adversarial Review Architecture for Multi-Model Technical Document Generation
Jun 13, 2026Zhiqiang Zhou, Junliang Dai, Xu LingMulti-Agent LLM Systems LLM Evaluation
Hunan Chemical Industry Vocational and Technical College, Hunan, China
Group Preference Collapse in Personalized Multimodal Large Language Models
Jun 12, 2026Fan Lyu, Wenqi Zhang, Joost van de WeijerMultimodal Large Language Models Personalized Language Models
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, Spain, 08193
Characterizing Cultural Localization in AI-Generated Stories
Jun 12, 2026Shaily Bhatt, Supriti Vijay, Jeremiah Milbauer +1Cultural Bias in Language Models Language Model Bias Evaluation
Carnegie Mellon University
LessonBench-V1: A Benchmark Dataset for Evaluating AI Lesson Generation Agents
Jun 12, 2026Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle +2Educational Assessment Generative AI in Education
Department of Computer Science, Nottingham Trent University, Nottingham, UK · Department of Physics and Mathematics, Nottingham Trent University, Nottingham, UK
IVIE: A Neuro-symbolic Approach to Incremental and Validated Generation of Interactive Fiction Worlds
Jun 11, 2026Micaela Vaucher, Santiago Silveira, Santiago Góngora +1Computational Creativity Procedural Content Generation
Low-Latency Real-Time Audio Game Commentary System via LLM-Based Parallel Text Generation
Jun 11, 2026Ryota Kawamatsu, Anum Afzal, Yuki Saito +5Text Generation
The University of Tokyo, Japan · National Institute of Advanced Industrial Science and Technology, Japan · Technical University of Munich, Germany +3
Agreement in Representation Space for Open-Ended Self-Consistency
Jun 10, 2026Paula Ontalvilla, Gorka Azkune, Aitor OrmazabalOpen-Ended Generation Self-Consistency Decoding
HiTZ Center - Ixa, University of the Basque Country (UPV/EHU)
IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking
Jun 8, 2026Zechen Sun, Yuyang Sun, Zecheng Tang +6Open-Ended Generation Long-Form Document Generation
Institute of Computer Science and Technology, Soochow University · 2Information Research Center of Military Science, PLA Academy of Military Science
Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusion
Jun 8, 2026Kuanlin Chen, Cheng-En OuAI in Education Generative AI in Education
Department of Industrial Engineering & Engineering Management Chung Yuan Christian University, Taiwan
Precision Is Not Faithfulness: Coverage-Aware Evaluation of Grounded Generation with a Complete Oracle
Jun 8, 2026Juan S. SantillanaContextual Faithfulness Factual Consistency Evaluation
Globant
Generating in the Limit with Infinitely Many Hallucinations
Jun 8, 2026Irene Strauss, Alexandra Butoi, Ryan CotterellHallucination in Language Models Text Generation
ETH Zürich
Unified Energy for Invariant and Independent Decoding in Diffusion Language Models
Jun 8, 2026Yuchen Yan, Minkai Xu, Zaiquan Yang +1Energy-Based Models Diffusion Language Model Decoding
Department of Computer Science National University of Singapore · Department of Computer Science Stanford University · Department of Computer Science City University of Hong Kong
PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus
Jun 8, 2026Gen Li, Yuanze Hu, Zhichao Yang +10Medical Diagnosis Clinical Reasoning
Beihang University · Baidu · ByteDance +2
TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering
Jun 7, 2026Ryandito Diandaru, Ikhlasul Akmal Hanif, Fadli Aulawi Al Ghiffari +2Language Model Steering Diffusion Language Models
MBZUAI
Beyond Individual Personas: Aligning Synthetic Dialogue to Population-Level Behavior Distributions
Jun 5, 2026Xinyi Liu, Rinat Khaziev, Hooshang Nayyeri +3Synthetic Data Generation Persona-Conditioned Generation
1Amazon · University of Illinois Urbana–Champaign · University College London
TA-RAG: Tone-Aware Retrieval-Augmented Generation for Peer-Support Health Communication
Jun 5, 2026Yong-Bin Kang, Anthony McCoskerRetrieval-Augmented Generation Healthcare
Swinburne University of Technology · Melbourne, Victoria, Australia
TextWand: A Unified Framework for Scene Text Editing
Jun 4, 2026Shuyu Wang, Zhile Guan, Hongxiu Chen +5Text-Guided Image Editing Text Generation
School of Electronic and Computer Engineering, Peking University, Shenzhen, China.
Interpreting Style Representations via Style-Eliciting Prompts
Jun 4, 2026Junghwan Kim, David JurgensLLM Prompting LLM Interpretability
University of Michigan
Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation
Jun 3, 2026Ahmed Alansary, Molham Mohamed, Ali HamdiMedical QA Curriculum Learning
Faculty of Computer Science MSA University Giza, Egypt

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The Significance of Style Diversity in Annotation-Free Synthetic Data Generation

Zahra Abbasiantaeb, Zeno Belligoli, Omar Essam, Mohammad Aliannejadi

University of Amsterdam, Amsterdam, The Netherlands · Booking.com, Amsterdam, The Netherlands

Synthetic Data Generation Controllable Text Generation Intent Classification Text Generation

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Generating high-utility synthetic data for intent classification typically requires human-annotated seed data, which is often unavailable in fast-paced industrial settings. In this paper, we propose a framework for synthetic dialogue generation that works entirely without human-annotated data, relying solely on intent definitions. Our proposed dialogue generation framework utilizes two different types of topic and style attributes to improve data diversity. Also, we propose two novel post-hoc stylization models called Univ and Exam to transform synthetic LLM-generated utterances into more varied, human-like linguistic styles. To enhance data quality, we utilize an LLM-as-a-judge filtering process. Experimental results on both industrial and public datasets demonstrate that the proposed approach achieves up to 93.3% of the performance obtained using human-annotated training data. Crucially, the findings reveal that style diversity is more critical than topic diversity for synthetic data utility, as it prevents models from learning spurious stylistic correlations. Furthermore, the study shows that incorporating style attributes during the generation process is more effective than post-hoc style adaptation.

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