Cover Image

Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Minda, Du, Yilun, Wang, Mengyu
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Large Language Models Badly Generalize across Option Length, Problem Types, and Irrelevant Noun Replacements
by: Zhao, Guangxiang, et al.
Published: (2025)

Large Language Models Struggle in Token-Level Clinical Named Entity Recognition
by: Lu, Qiuhao, et al.
Published: (2024)

How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
by: Seddik, Mohamed El Amine, et al.
Published: (2024)

SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
by: Jiang, Dongwei, et al.
Published: (2024)

Reasoning with Sampling: Your Base Model is Smarter Than You Think
by: Karan, Aayush, et al.
Published: (2025)

Frontier LLMs Still Struggle with Simple Reasoning Tasks
by: Malek, Alan, et al.
Published: (2025)

Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution
by: Zhao, Haiyan, et al.
Published: (2024)

All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language
by: Guo, Shiyuan, et al.
Published: (2025)

Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
by: Bansal, Hritik, et al.
Published: (2024)

Variation in Verification: Understanding Verification Dynamics in Large Language Models
by: Zhou, Yefan, et al.
Published: (2025)

The Randomness Floor: Measuring Intrinsic Non-Randomness in Language Model Token Distributions
by: Hryszko, Jarosław
Published: (2026)

Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis
by: Li, Daoyang, et al.
Published: (2024)

PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
by: Zou, Jiaru, et al.
Published: (2024)

player2vec: A Language Modeling Approach to Understand Player Behavior in Games
by: Wang, Tianze, et al.
Published: (2024)

SPIN: Sparsifying and Integrating Internal Neurons in Large Language Models for Text Classification
by: Jiao, Difan, et al.
Published: (2023)

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models
by: Xue, Jiaqi, et al.
Published: (2024)

Comprehensive Reassessment of Large-Scale Evaluation Outcomes in LLMs: A Multifaceted Statistical Approach
by: Sun, Kun, et al.
Published: (2024)

PAT: Pruning-Aware Tuning for Large Language Models
by: Liu, Yijiang, et al.
Published: (2024)

On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)

All Language Models Large and Small
by: Chen, Zhixun, et al.
Published: (2024)

Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
by: Hu, Zhiyuan, et al.
Published: (2024)

When Bad Data Leads to Good Models
by: Li, Kenneth, et al.
Published: (2025)

Assessing the Macro and Micro Effects of Random Seeds on Fine-Tuning Large Language Models
by: Bui, Nghia, et al.
Published: (2025)

$T^2$ of Thoughts: Temperature Tree Elicits Reasoning in Large Language Models
by: Cai, Chengkun, et al.
Published: (2024)

Sparse is Enough in Fine-tuning Pre-trained Large Language Models
by: Song, Weixi, et al.
Published: (2023)

CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities
by: Mao, Yujun, et al.
Published: (2024)

IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
by: Li, Yixiao, et al.
Published: (2025)

Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers
by: Du, Yixuan, et al.
Published: (2026)

Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning
by: Yue, Murong, et al.
Published: (2023)

Transformers Struggle to Learn to Search
by: Saparov, Abulhair, et al.
Published: (2024)

RuAG: Learned-rule-augmented Generation for Large Language Models
by: Zhang, Yudi, et al.
Published: (2024)

Dist2ill: Distributional Distillation for One-Pass Uncertainty Estimation in Large Language Models
by: Zhao, Yicong, et al.
Published: (2025)

Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models
by: Messina, Alberto, et al.
Published: (2026)

Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models
by: Sun, Shaoning, et al.
Published: (2026)

SambaLingo: Teaching Large Language Models New Languages
by: Csaki, Zoltan, et al.
Published: (2024)

Systematic Outliers in Large Language Models
by: An, Yongqi, et al.
Published: (2025)

Generative Evaluation of Complex Reasoning in Large Language Models
by: Lin, Haowei, et al.
Published: (2025)

EvoLM: In Search of Lost Language Model Training Dynamics
by: Qi, Zhenting, et al.
Published: (2025)

Generalized Category Discovery with Large Language Models in the Loop
by: An, Wenbin, et al.
Published: (2023)

BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
by: He, Liulu, et al.
Published: (2025)