Diverse Text Generation

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6 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 34

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Improving Diversity in LLM Short Story Generation
Oct 5, 2026Zahra Solati Dehkordi, Vasileios LamposDiverse Text Generation Language Model Post-Training
Centre for Artificial Intelligence Department of Computer Science University College London, UK
Architectural Sampling: Test-Time Scaling via Computational Diversity in Frozen Vision-Language Models
Oct 1, 2026Akshit Singh, Shyam Marjit, Wei Lin +2Test-Time Scaling for VLMs Large Vision-Language Models
Dartmouth College · IISc Bangalore · Xero
Gacha Decoding: Eliciting Diverse Generations Through Instruction Following
Oct 1, 2026Scott Geng, Yufei Zhang, Joseph Lee +3Language Model Decoding Diverse Text Generation
University of Washington · Meta Superintelligence Labs
Mixture of Decoders for Diverse Dialog Response Generation
Sep 30, 2026Wenchao DuSpoken Dialogue Systems Diverse Text Generation
Microsoft Corporation Redmond, WA 98052
Self-Repulsive Sampling for Diffusion Language Models
Sep 30, 2026Michael Helcig, Martin JaggiDiffusion Language Model Decoding Diverse Text Generation
ETH Zürich · EPFL
One Model Is Not a Crowd: Multi-LLM and Aspect-Conditioned Diverse Comment Generation
Sep 27, 2026Nafis Irtiza Tripto, Delvin Ce Zhang, Mahjabin Nahar +1LLM Evaluation Controllable Text Generation
College of Information Sciences and Technology, Pennsylvania State University, PA, USA · University of Sheffield, Sheffield, UK
Beyond Truncation: Rethinking LLM Decoding as Ensemble Pruning
Sep 16, 2026Dunyao Xue, Chengshuo Du, Zhengbo Wang +2Language Model Decoding Diverse Text Generation
Institute of Statistics and Big Data, Renmin University of China, Beijing, China · Big Data and Responsible Artificial Intelligence for National Governance, Renmin University of China, Beijing, China · Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China
Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?
Sep 1, 2026Tien Anh Nguyen, Khanh-Binh Nguyen, Van Dai Do +2Reasoning Diversity Multi-Agent Debate
Deakin Applied Artificial Intelligence Initiative, Deakin University, Australia
Novels generated by language models show compressed formal variation
Aug 12, 2026Mehdy Sedaghat Payam, Justin QuinnLLM Evaluation Long-Form Document Generation
University of Maryland · University of West Bohemia · Charles University
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
Aug 7, 2026Ananya Sahu, Mohit Bansal, Elias Stengel-EskinInstruction Tuning Diverse Text Generation
Columbia · UNC Chapel Hill · University of Texas at Austin
Human diversity fuels collective creativity that large language models cannot simulate or sustain
Jul 29, 2026Mengchen Dong, Hiromu YakuraComputational Creativity Diverse Text Generation
Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
Structured Output Collapses Answer Diversity Across 44 Language Models
Jul 20, 2026Tapan ParikhLLM Evaluation Structured Prompting
Cornell Tech
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
Jun 24, 2026Andrei Liviu Nicolicioiu, Mohammad Pezeshki, Aaron CourvilleOn-Policy Self-Distillation Language Model Distillation
Mila · Université de Montréal · FAIR at Meta +1
CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories
Jun 21, 2026Anneliese Brei, Abhisheik Sharma, Nicholas Sanaie +2LLM Evaluation Diverse Text Generation
UNC Chapel Hill · Georgia Institute of Technology · University of Michigan
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
Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Jun 9, 2026Cheng Zhang, Rui Xin, Chudi ZhongOpen-Ended Generation Controllable Text Generation
UNC Chapel Hill · University of Washington
Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization
Jun 8, 2026Muhammad Haris Khan, Joel westerComputational Creativity Diverse Text Generation
University of Copenhagen Denmark
"I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise
Jun 1, 2026Matthew Khoriaty, David Williams-King, Shi FengDiverse Text Generation Generative AI Evaluation
ERA Fellowship · George Washington University
Fine-Tuning Improves Information Conveyance in Language Models
May 29, 2026Yuwei Cheng, Weiyi Tian, Haifeng XuLanguage Model Generation Evaluation LLM Fine-Tuning
Department of Data Science University of Chicago Chicago, IL 60637 · Department of Computer Science University of Chicago Chicago, IL 60637
Anchorless Diversification for Parallel LLM Ideation
May 28, 2026Fares Nabil Ibrahim, Nafis Saami Azad, Raiyan Abdul BatenLLM Inference Efficiency LLM Inference
Bellini College of Artificial Intelligence, Cybersecurity, and Computing, University of South Florida, USA
Semantic Flow Regularization: Teaching LLMs to Generate Diverse Yet Coherent Responses
May 27, 2026Kerui Peng, Feifei Li, Xingyu Fan +1Controllable Text Generation Diverse Text Generation
WeChat, Tencent Inc., Beijing, China
Lost in Sampling: Assessing Lexical Reachability in LLMs via the Word Coverage Score (WCS)
May 26, 2026Samer Awad, Javier Conde, Carlos Arriaga +3LLM Evaluation Language Model Decoding
Information and Processing Telecommunications Center Universidad Politécnica de Madrid Madrid, Spain · Politecnico di Milano Milano, Italy · Banco de España Madrid, Spain
Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories
May 26, 2026Sil Hamilton, David MimnoLLM Alignment Diverse Text Generation
Department of Information Science Cornell University
Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion
May 21, 2026Meimingwei Li, Yuanhao Ding, Esteban Garces Arias +1LLM Interpretability LLM Fine-Tuning
Department of Statistics, LMU Munich · School of Computer and Information Engineering, Henan University · Munich Center for Machine Learning (MCML)
Pairwise Preference Reward and Group-Based Diversity Enhancement for Superior Open-Ended Generation
May 18, 2026Guining Cao, Jiaxin Peng, Chu Zeng +3Pairwise Preference Learning Open-Ended Generation
Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd · School of Software and Microelectronics, Peking University · Tsinghua University
Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs
May 11, 2026Amin Banayeeanzade, Qingchuan Yang, Dhruv Tarsadiya +6LLM Evaluation Language Model Decoding
♠University of Southern California · ♣Capital One
Annotations Mitigate Post-Training Mode Collapse
May 11, 2026Jacob Mitchell Springer, Madhu Advani, Lukas Aichberger +7Supervised Fine-Tuning Diverse Text Generation
Carnegie Mellon University · Apple · Johannes Kepler University Linz
Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution
May 10, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuEvolutionary Optimization Large Language Model-Guided Optimization
The University of Hong Kong, China · Stellaris AI Limited, China
Large Language Models Explore by Latent Distilling
Apr 27, 2026Yuanhao Zeng, Ao Lu, Lufei Li +3Language Model Decoding Test-Time Training
School of Information Science and Technology, ShanghaiTech University, Shanghai, China · State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China.
A Universal Avoidance Method for Diverse Multi-branch Generation
Apr 19, 2026Kyeongman Park, Minha Jhang, Kyomin JungDiverse Text Generation
Seoul National University

Oct 5, 2026, cs.CLJ/K move · Enter open · S save

Improving Diversity in LLM Short Story Generation

Zahra Solati Dehkordi, Vasileios Lampos

Centre for Artificial Intelligence Department of Computer Science University College London, UK

Diverse Text Generation Language Model Post-Training Text Generation

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Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative short story generation. Drawing on established writing conventions and known LLM limitations, we target variation in genre, tone, style, and named entities. To promote diversity across these dimensions, we introduce DivLM, an LLM post-training framework consisting of two phases. First, we perform continued pre-training on a creative writing corpus and restore instruction-following capabilities using weight residuals. We then apply reinforcement learning with a custom, composite reward function that jointly maximizes diversity across the targeted narrative dimensions while maintaining response quality. Our empirical results on two LLM families show that DivLM increases diversity metrics by more than 9% on average compared to alternative approaches, while preserving instruction following, overall response quality, and similarity to human outputs.

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