Output Diversity in Language Models

Latest papers

32 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

MemBubble: Benchmarking How Agent Memory Shapes LLM Response Diversity
Oct 18, 2026Jiajun Tan, Fei Sun, Xinyu Ma +6LLM Agent Memory Output Diversity in Language Models
State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Baidu Inc.
Language Models are Lazy Poets: Creative Mode Collapse at Scale
Oct 16, 2026Jacob A. Matthews, Sil HamiltonOutput Diversity in Language Models Mode Collapse
Cornell University
Analyzing Occupational Gender Bias across the LLM Training Lifecycle
Oct 16, 2026Manthan Gadhia, Alexander Hoyle, Karolina StańczakGender Bias in Language Models Language Model Post-Training
Department of Humanities, Social, and Political Sciences, ETH Zürich · TU Wien, Complexity Science Hub · ETH AI Center, ETH Zürich
The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language?
Oct 12, 2026Xaver Raphael Davey, Shubhra Mishra, Anton Adelöw +1Output Diversity in Language Models Homogenization in Language Models
KTH Royal Institute of Technology
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
How Strong Is the Evidence for the Artificial Hivemind? Reevaluating Evidence for the Open-Ended Homogeneity of Language Models
Sep 27, 2026Rylan Schaeffer, Brando Miranda, Joshua Kazdan +2Output Diversity in Language Models Homogenization in Language Models
Stanford Computer Science
Improving the Diversity of LLM Outputs without a Trade-off
Sep 27, 2026Ryoma SatoOutput Diversity in Language Models Language Model Decoding
National Institute of Informatics
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Sep 24, 2026Sang Bin Moon, Nicole Cho, Daniel Borrajo +2Output Diversity in Language Models Diverse Text Generation
School of Electrical and Computer Engineering Purdue University West Lafayette, IN 47909, USA · J.P. Morgan AI Research New York, NY 10172, USA
Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs
Sep 15, 2026Kirill Skobelev, Eric Fithian, X. Y. HanOutput Diversity in Language Models Mode Collapse
Northwestern University · University of Chicago
Evaluating Multiple LLM Generations with Validated Task Coverage
Aug 25, 2026Florian Le Bronnec, Rio YokotaOutput Diversity in Language Models LLM Evaluation
RIKEN Center for Computational Science, Tokyo, Japan
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
Beyond "AI Language": The case for the idiolectal nature of LLM output
Aug 6, 2026Karolina Rudnicka, Thomas Stephan JuzekLanguage Model Fingerprinting Output Diversity in Language Models
Faculty of Languages, University of Gdańsk, Poland · Department of Scientific Computing, Florida State University, United States
On the Diversity of Analogy Making in Large Language Models
Aug 4, 2026Yuanhao Shen, Daniel Xavier de Sousa, Caio César Sifuentes Barcelos +2LLM Evaluation Analogical Reasoning for Language Models
Department of Electrical and Computer Engineering & Ingenuity Labs Research Institute, Queen’s University, Canada · Instituto Federal de Goiás, Anápolis, Brazil · National Research Council Canada.
Structured Output Collapses Answer Diversity Across 44 Language Models
Jul 20, 2026Tapan ParikhLLM Evaluation Structured Prompting
Cornell Tech
The One-Word Census: Answer-Choice Conformity Across 105 Language Models
Jul 14, 2026Tapan ParikhLLM Evaluation Language Model Generation Evaluation
Cornell Tech
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
"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 Modeling LLM Fine-Tuning
Department of Data Science University of Chicago Chicago, IL 60637 · Department of Computer Science University of Chicago Chicago, IL 60637
Recovering Diversity Without Losing Alignment: A DPO Recipe for Post-Trained LLMs
May 28, 2026Vinay Samuel, Yapei Chang, Mohit IyyerLLM Alignment Direct Preference Optimization
University of Maryland, College Park
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
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
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.
Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations
Apr 20, 2026Emily Reif, Claire Yang, Jared Hwang +3Open-Ended Generation Data Visualization
University of Washington, Seattle, WA, USA · Allen Institute for AI
Where does output diversity collapse in post-training?
Apr 17, 2026Constantinos Karouzos, Xingwei Tan, Nikolaos AletrasOutput Diversity in Language Models Language Model Post-Training
School of Computer Science University of Sheffield, UK
Dynamic Context Evolution for Scalable Synthetic Data Generation
Apr 8, 2026Ryan Lingo, Rajeev ChhajerRepetition in Language Generation Output Diversity in Language Models
Honda Research Institute, USA, Inc.
Magic, Madness, Heaven, Sin: LLM Output Diversity is Everything, Everywhere, All at Once
Apr 2, 2026Harnoor DhingraOutput Diversity in Language Models Language Model Generation Evaluation
Microsoft
Free Lunch for Pass@ $k$ ? Low Cost Diverse Sampling for Diffusion Language Models
Mar 5, 2026Sean Lamont, Christian Walder, Paul Montague +2Diversity-Aware Sampling Output Diversity in Language Models
Australian National University · Defence Science and Technology Group · Google DeepMind +1
Asymptotic Universal Alignment: A New Alignment Framework via Test-Time Scaling
Jan 13, 2026Yang Cai, Weiqiang ZhengLLM Alignment Test-Time Scaling
Yale University
Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)
Oct 27, 2025Liwei Jiang, Yuanjun Chai, Margaret Li +7Open-Ended Generation LLM Evaluation
University of Washington · Allen Institute for Artificial Intelligence · Carnegie Mellon University +3
Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
Oct 1, 2025Jiayi Zhang, Simon Yu, Derek Chong +4LLM Prompting Model Collapse
Northeastern University · Stanford University · West Virginia University

Oct 18, 2026J/K move · Enter open · S save

MemBubble: Benchmarking How Agent Memory Shapes LLM Response Diversity

Jiajun Tan, Fei Sun, Xinyu Ma, Shixuan Zhang+5

State Key Laboratory of AI Safety, Institute of Computing Technology, CAS · University of Chinese Academy of Sciences · Baidu Inc.

LLM Agent Memory Output Diversity in Language Models Memory Agent Benchmarks

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Memory serves as the core component of LLM-based agents, enabling personalized responses via extracting and using user preferences from past interactions. However, its use may reduce response diversity, thereby narrowing the range of information available to users. Existing evaluations rely largely on synthetic interactions and cover few memory systems, leaving the effects of memory on response diversity insufficiently evaluated in real-world scenarios. We introduce MemBubble, a benchmark of 1000 instances built on preferences extracted from real user-assistant conversations. To measure the extent to which memory concentrates response distributions, we construct test queries in scenarios distinct from user preferences while preserving topical relevance through shared preference categories. Across multiple LLMs and memory mechanisms, entropy decreases by 34.7% on average compared with directly providing the full interaction history, highlighting the important role of memory processing in diversity loss. We also explore simple user-side interventions and changes in information availability but find that these do not effectively close the diversity gap. Our findings highlight the need to consider preserving diversity and user access to information alongside improvements in personalization when designing and using agent memory systems.