Large Language

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Large Language Models Fail

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What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models
Oct 5, 2025Dustin Wright, Sarah Masud, Jared Moore +5Epistemic Orientation Large Language
Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks
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Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity
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Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
Sep 29, 2025Yunhao Liang, Pujun Zhang, Yuan Qu +3Multimodal Graph Foundation Learning Combinatorial Optimization
CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models
Sep 26, 2025Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey +4Large Language Temporal Anchor
Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank
Sep 25, 2025Yiheng Tao, Yihe Zhang, Matthew Dearing +4Large Language Ranking
Training-free Truthfulness Detection via Sparse MLP Value Vectors
Sep 22, 2025Runheng Liu, Heyan Huang, Xingchen Xiao +2Truth Large Language Model Reliability
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Sep 12, 2025Woojung Song, Dongmin Choi, Yoonah Park +3Psychometrics Personas
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
Sep 10, 2025Yisong Zhang, Ran Cheng, Guoxing Yi +1Evolutionary Search Evolutionary Algorithms
Beyond Semantic Similarity: Reducing Unnecessary API Calls via Behavior-Aligned Retriever
Aug 20, 2025Yixin Chen, Ying Xiong, Shangyu Wu +4Large Language Model Calls Application Programming Interfaces
Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
Aug 9, 2025Jinhwa Kim, Ian G. HarrisLarge Language Models Fail Adversaries
Somatic in the East, Psychological in the West?: Investigating Clinically-Grounded Cross-Cultural Depression Symptom Expression in LLMs
Aug 5, 2025Shintaro Sakai, Jisun An, Migyeong Kang +1Depression Large Language Models Fail
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
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A Technical Survey of Reinforcement Learning Techniques for Large Language Models
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RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
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"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Jun 11, 2025Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei ChooMental Health Large Language Models Fail
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
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TIIF-Bench: How Does Your T2I Model Follow Your Instructions?
Jun 2, 2025Xinyu Wei, Jinrui Zhang, Zeqing Wang +4Text-To-Image Models Text-To-Image
RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
May 25, 2025Zhengkang Guo, Wenhao Liu, Mingchen Xie +13Instruction-Tuned Large Language Models Instruction
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Learning by Surprise: Adaptive Mitigation of Model Collapse in Large Language Models
Oct 16, 2024Daniele Gambetta, Gizem Gezici, Fosca Giannotti +3Collapse Generative Artificial Intelligence
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Refusal Beyond a Single Direction: A Preliminary Comparison of Diff-in-Means and INLP
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On The Effectiveness-Fluency Trade-Off In LLM Conditioning: A Systematic Study
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Diffract: Spectral View of LLM Domain Adaptation
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LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains
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Measuring Pragmatic Influence in Large Language Model Instructions
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How LLMs Follow Instructions: Skillful Coordination, Not a Universal Mechanism
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SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Date pendingJhon Lopez, Carlos Hinojosa, Bernard GhanemAgentic Video Generation Animation

Oct 5, 2025 · cs.CLJ/K move · Enter open · S save

What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models

Dustin Wright, Sarah Masud, Jared Moore, Srishti Yadav+4

University of Copenhagen · Stanford University · University of Colorado Boulder · Microsoft Research

Large language models (LLMs) are increasingly used as primary knowledge sources, yet their epistemic diversity - defined as the diversity of real-world claims in their outputs - has never been measured. Low epistemic diversity would pose a risk of knowledge collapse as homogeneous LLMs mediate a shrinking in the range of accessible information over time. The dominant paradigm is that overall LLM diversity is low, but this is always with respect to a single point in time, with no reference baseline or consideration for variation across countries. We address this gap in knowledge by performing the first systematic study of epistemic diversity in LLMs across time and cultural context, testing 27 LLMs on 155 topics covering 12 countries, resulting in 1.7M responses and 70M individual claims. We find that epistemic diversity has increased substantially over the past three years, a positive counter to recent diversity pessimism. However, despite progress, we find that every system is less diverse than a search baseline. This gap is not uniform: RAG can improve diversity, while large models are counterintuitively less diverse than smaller ones. Moreover, LLM parametric knowledge systematically reflects English over local-language knowledge for country specific topics. Together, these results demonstrate that while progress on epistemic diversity is tangible, it is insufficient and unevenly distributed.

Epistemic Orientation Large Language

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