The exponential distribution of the order of demonstrative, numeral, adjective and noun

arXiv — cs.CLWednesday, November 5, 2025 at 5:00:00 AM
Recent research has examined the order of demonstratives, numerals, adjectives, and nouns within noun phrases, focusing on all 24 possible permutations of these elements (F1). The study aimed to identify the statistical distribution that best models the frequency of these orders (F2). By comparing different distribution models, the research found that an exponential distribution provides a more accurate fit than a power law distribution (F3). This conclusion supports the view that the exponential distribution better captures the observed data patterns in linguistic ordering (A1). The findings contribute to ongoing discussions in linguistics regarding the underlying principles governing word order in noun phrases (F4). This work, published on arXiv in the computer science and computational linguistics category, adds quantitative evidence to debates about language structure modeling.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Bridging Hidden States in Vision-Language Models
PositiveArtificial Intelligence
Vision-Language Models (VLMs) are emerging models that integrate visual content with natural language. Current methods typically fuse data either early in the encoding process or late through pooled embeddings. This paper introduces a lightweight fusion module utilizing cross-only, bidirectional attention layers to align hidden states from both modalities, enhancing understanding while keeping encoders non-causal. The proposed method aims to improve the performance of VLMs by leveraging the inherent structure of visual and textual data.
Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning
PositiveArtificial Intelligence
The paper titled 'Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning' introduces a new method called Bias-REstrained Prefix Representation FineTuning (BREP ReFT). This approach aims to enhance the mathematical reasoning capabilities of models by addressing the limitations of existing Representation finetuning (ReFT) methods, which struggle with mathematical tasks. The study demonstrates that BREP ReFT outperforms both standard ReFT and weight-based Parameter-Efficient finetuning (PEFT) methods through extensive experiments.
Transformers know more than they can tell -- Learning the Collatz sequence
NeutralArtificial Intelligence
The study investigates the ability of transformer models to predict long steps in the Collatz sequence, a complex arithmetic function that maps odd integers to their successors. The accuracy of the models varies significantly depending on the base used for encoding, achieving up to 99.7% accuracy for bases 24 and 32, while dropping to 37% and 25% for bases 11 and 3. Despite these variations, all models exhibit a common learning pattern, accurately predicting inputs with similar residuals modulo 2^p.
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions
PositiveArtificial Intelligence
Higher-order Neural Additive Models (HONAMs) have been introduced as an advancement over Neural Additive Models (NAMs), which are known for their predictive performance and interpretability. HONAMs address the limitation of NAMs by effectively capturing feature interactions of arbitrary orders, enhancing predictive accuracy while maintaining interpretability, crucial for high-stakes applications. The source code for HONAM is publicly available on GitHub.