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Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry? [47.71394430680399]
Large Language Models (LLMs) have achieved significant progress across a wide range of natural language processing (NLP) tasks.<n>Modern Chinese poetry necessitate a distinct form of reasoning for effective comprehension.<n>We propose Peony, the first benchmark specifically designed for evaluating the poetic logic of modern Chinese poetry.
arXiv Detail & Related papers (2026-08-22T07:52:00Z)
Instruction-Guided Poetry Generation in Arabic and Its Dialects [44.29808445432812]
We present a large-scale, carefully curated instruction-based dataset in Modern Standard Arabic (MSA) and various Arabic dialects.<n>This dataset enables tasks such as writing, revising, and continuing poems based on predefined criteria, including style and rhyme.<n>Experiments show that fine-tuning LLMs on this dataset yields models that can effectively generate poetry that is aligned with user requirements.
arXiv Detail & Related papers (2026-04-30T11:58:13Z)
Floating or Suggesting Ideas? A Large-Scale Contrastive Analysis of Metaphorical and Literal Verb-Object Constructions [53.690096725532726]
We analyze 297 English verb-object pairs (e.g., float idea vs. suggest idea) in 2M corpus sentences.<n>Using five NLP tools, we extract 2,293 cognitive and linguistic features capturing affective, lexical, syntactic, and discourse-level properties.<n>Cross-pair results show literal contexts have higher lexical frequency, cohesion, and structural regularity, while metaphorical contexts show greater affective load, imageability, lexical diversity, and constructional specificity.
arXiv Detail & Related papers (2026-04-09T14:08:57Z)
Can ChatGPT Really Understand Modern Chinese Poetry? [41.340415709375186]
ChatGPT has demonstrated remarkable capabilities on both poetry generation and translation.<n>Previous poetry-related work merely analyzed experimental outcomes without addressing fundamental issues of comprehension.<n>This paper introduces a comprehensive framework for evaluating ChatGPT's understanding of modern poetry.
arXiv Detail & Related papers (2026-03-21T15:14:43Z)
Artificial Intelligence for Sentiment Analysis of Persian Poetry [0.0]
We employ multiple Bidirectional encoder representations from transformers (BERT) and Generative Pre-trained Transformer (GPT) based language models to analyze the works of two prominent Persian poets: Jalal al-Din Muhammad Rumi (Rumi) and Parvin E'tesami.<n>Results of our sentiment analysis revealed that in general, Rumi's poems express happier sentiments compared to Parvin E'tesami's poems.
arXiv Detail & Related papers (2026-03-11T19:34:23Z)
Creating a digital poet [8.593632029412502]
We report a seven-month poetry workshop in which a large language model was shaped into a digital poet.<n>The model developed a distinctive style and a coherent corpus, supported by quantitative and qualitative analyses.<n>After the workshop, a commercial publisher released a poetry collection authored by the model.
arXiv Detail & Related papers (2026-02-18T16:25:10Z)
From Ghazals to Sonnets: Decoding the Polysemous Expressions of Love Across Languages [0.0]
This paper delves into the intricate world of Urdu poetry, exploring its thematic depths through a lens of polysemy.<n>By focusing on the nuanced differences between three seemingly synonymous words (pyaar, muhabbat, and ishq) we expose a spectrum of emotions and experiences unique to the Urdu language.
arXiv Detail & Related papers (2025-10-17T12:00:09Z)
A Computational Approach to Style in American Poetry [19.41186389974801]
We develop a method to assess the style of American poems and to visualize a collection of poems in relation to one another. qualitative poetry criticism helped guide our development of metrics that analyze various orthographic, syntactic, and phonemic features. Our method has potential applications to academic research of texts, to research of the intuitive personal response to poetry, and to making recommendations to readers based on their favorite poems.
arXiv Detail & Related papers (2023-10-13T18:49:14Z)
Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches [7.021140304091526]
This paper introduces a framework called textitAshaar, which encompasses a collection of datasets and pre-trained models designed specifically for the analysis and generation of Arabic poetry. The pipeline established within our proposed approach encompasses various aspects of poetry, such as meter, theme, and era classification. As part of this endeavor, we provide four datasets: one for poetry generation, another for diacritization, and two for Arudi-style prediction.
arXiv Detail & Related papers (2023-07-12T15:07:16Z)
A Method to Judge the Style of Classical Poetry Based on Pre-trained Model [13.899056358137287]
This paper builds the most perfect data set of Chinese classical poetry at present, trains a BART-poem pre-trained model on this data set, and puts forward a generally applicable poetry style judgment method. Experiments show that the judgment results of the tested poetry work are basically consistent with the conclusions given by critics of previous dynasties, verify some avant-garde judgments of Mr. Qian Zhongshu, and better solve the task of poetry style recognition in the Tang and Song dynasties.
arXiv Detail & Related papers (2022-11-09T03:11:15Z)
Generation of Chinese classical poetry based on pre-trained model [1.6114012813668934]
This paper mainly tries to use BART and other pre training models to generate metrical poetry text. It developed a set of AI poetry Turing problems, it was reviewed by a group of poets and poetry writing researchers. The model of poetry generation studied by the author generalizes works that cannot be distinguished from those of advanced scholars.
arXiv Detail & Related papers (2022-11-04T16:05:31Z)
PoeLM: A Meter- and Rhyme-Controllable Language Model for Unsupervised Poetry Generation [42.12348554537587]
Formal verse poetry imposes strict constraints on the meter and rhyme scheme of poems. Most prior work on generating this type of poetry uses existing poems for supervision. We propose an unsupervised approach to generate poems following any given meter and rhyme scheme.
arXiv Detail & Related papers (2022-05-24T17:09:55Z)
Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution [74.27826764855911]
We employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. Our experiments, carried out on three different datasets, using two different machine learning methods, show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors.
arXiv Detail & Related papers (2021-10-27T06:25:31Z)
It's not Rocket Science : Interpreting Figurative Language in Narratives [48.84507467131819]
We study the interpretation of two non-compositional figurative languages (idioms and similes) Our experiments show that models based solely on pre-trained language models perform substantially worse than humans on these tasks. We additionally propose knowledge-enhanced models, adopting human strategies for interpreting figurative language.
arXiv Detail & Related papers (2021-08-31T21:46:35Z)
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System [43.36560720793425]
Lingxi is a diversity-aware Chinese modern poetry generation system. We propose nucleus sampling with randomized head (NS-RH) algorithm. We find that even when a large portion of filtered vocabulary is randomized, it can actually generate fluent poetry.
arXiv Detail & Related papers (2021-08-27T03:33:28Z)
Urdu & Hindi Poetry Generation using Neural Networks [0.0]
The purpose of this work is to give an ode to the Urdu, Hindi poets, and helping them start their next line of poetry. A concern with creative works like this, especially in the literary context, is to ensure that the output is not plagiarized. This work also addresses the concern and makes sure that the resulting odes are not exact match with input data.
arXiv Detail & Related papers (2021-07-16T16:12:51Z)
Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals [69.76097138157816]
Probabilistic text generators have been used to produce fake scientific papers for more than a decade. Complex AI-powered generation techniques produce texts indistinguishable from that of humans. Some websites offer to rewrite texts for free, generating gobbledegook full of tortured phrases.
arXiv Detail & Related papers (2021-07-12T20:47:08Z)
CCPM: A Chinese Classical Poetry Matching Dataset [50.90794811956129]
We propose a novel task to assess a model's semantic understanding of poetry by poem matching. This task requires the model to select one line of Chinese classical poetry among four candidates according to the modern Chinese translation of a line of poetry. To construct this dataset, we first obtain a set of parallel data of Chinese classical poetry and modern Chinese translation.
arXiv Detail & Related papers (2021-06-03T16:49:03Z)
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework [88.57587722069239]
We propose a GPT-2 based framework for generating major types of Chinese classical poems. Preliminary results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content.
arXiv Detail & Related papers (2020-03-13T14:16:25Z)
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space [79.70053419040902]
We propose MixPoet, a novel model that absorbs multiple factors to create various styles and promote diversity. Based on a semi-supervised variational autoencoder, our model disentangles the latent space into some subspaces, with each conditioned on one influence factor by adversarial training. Experiment results on Chinese poetry demonstrate that MixPoet improves both diversity and quality against three state-of-the-art models.
arXiv Detail & Related papers (2020-03-13T03:31:29Z)

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