Predicting upcoming words is a core mechanism of language comprehension and may be quantified using Shannon entropy. There is currently no empirical consensus on how many human responses are required to obtain stable and unbiased entropy estimates at the word level. Moreover, large language models (LLMs) are increasingly used as substitutes for human norming data, yet their ability to reproduce stable human entropy remains unclear. Here, we address both issues using two large publicly available cloze datasets in German 1 and English 2. We implemented a bootstrap-based convergence analysis that tracks how entropy estimates stabilize as a function of sample size. Across both languages, more than 97% of sentences reached stable entropy estimates within the available sample sizes. 90% of sentences converged after 111 responses in German and 81 responses in English, while low-entropy sentences (<1) required as few as 20 responses and high-entropy sentences (>2.5) substantially more. These findings provide the first direct empirical validation for common norming practices and demonstrate that convergence critically depends on sentence predictability. We then compared stable human entropy values with entropy estimates derived from several LLMs, including GPT-4o, using both logit-based probability extraction and sampling-based frequency estimation, GPT2-xl/german-GPT-2, RoBERTa Base/GottBERT, and LLaMA 2 7B Chat. GPT-4o showed the highest correspondence with human data, although alignment depended strongly on the extraction method and prompt design. Logit-based estimates minimized absolute error, whereas sampling-based estimates were better in capturing the dispersion of human variability. Together, our results establish practical guidelines for human norming and show that while LLMs can approximate human entropy, they are not interchangeable with stable human-derived distributions.
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning
Singapore University of Technology and Design（新加坡科技设计大学）；DAMO Academy, Alibaba Group, Singapore（阿里巴巴集团达摩院（新加坡））；Nanyang Technological University, Singapore（南洋理工大学）；Hupan Lab（湖畔实验室）
Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understanding
Graduate School of Data Science, Seoul National University（首尔国立大学数据科学研究生院）；Dept. of Computer Science and Engineering, Seoul National University（首尔国立大学计算机科学与工程系）
SemPA: Improving Sentence Embeddings of Large Language Models through Semantic Preference Alignment
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computer Science and Electronic Engineering, University of Surrey（Surrey大学计算机科学与电子工程学院）
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（中国科学技术大学认知智能国家重点实验室）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际 Graduate School）；Zhejiang University（浙江大学）
Jailbreak Instruction-Tuned LLMs via end-of-sentence MLP Re-weighting
Peking University（北京大学）；School of Science（理学院）；Westlake University（西湖大学）；Beijing International Center for Mathematical Research（北京国际数学研究中心）；Center for Machine Learning Research（机器学习研究中心）
Comparing Human and Language Models Sentence Processing Difficulties on Complex Structures
Blavatnik School of Computer Science, Tel Aviv University, Israel（巴尔-艾塔夫大学计算机科学学院）；Department of Linguistics, Tel Aviv University, Israel（巴尔-艾塔夫大学语言学系）；Sagol School of Neuroscience, Tel Aviv University, Israel（巴尔-艾塔夫大学神经科学学院）
Spatial Coordinates as a Cell Language: A Multi-Sentence Framework for Imaging Mass Cytometry Analysis
Department of Computer Science, The University of North Carolina at Chapel Hill（计算机科学系，北卡罗来纳大学教堂山分校）；Computational Medicine Program, The University of North Carolina at Chapel Hill（计算医学项目，北卡罗来纳大学教堂山分校）；Department of Genetics, The University of North Carolina at Chapel Hill（遗传学系，北卡罗来纳大学教堂山分校）
Rethinking the Understanding Ability across LLMs through Mutual Information
Department of Industrial and Systems Engineering（工业与系统工程系）；University of Houston（休斯敦大学）；Bakar Computational Health Sciences Institute（巴卡计算健康科学研究所）；UCSF
BLESS: Benchmarking Large Language Models on Sentence Simplification
University of Zurich（苏黎世大学）；National Tsing Hua University（国立清华大学）；Idiap Research Institute（Idiap研究所）；University of Manchester（曼彻斯特大学）；University of Maryland（马里兰大学）；Cohere（Cohere公司）；Cardiff University（卡迪夫大学）；Manchester Metropolitan University（曼彻斯特城市大学）
Beyond the Sentence: A Survey on Context-Aware Machine Translation with Large Language Models
Department of Computer Science and Engineering, Indian Institute of Technology Patna, India（印度理工学院帕纳瓦分校计算机科学与工程系）；Wipro AI, Lab45, London, UK（Wipro AI，Lab45，伦敦，英国）；School of AI and Data Science, Indian Institute of Technology Jodhpur, India（印度理工学院朱罗普分校人工智能与数据科学学院）
