Recent advances in language models (LMs), have demonstrated significant efficacy in tasks related to the arts and humanities. While LMs have exhibited exceptional performance across a wide range of natural language processing tasks, there are notable challenges associated with their utilization on small datasets and their ability to replicate more creative human capacities. In this study, we aim to address these challenges by training a Persian classical poetry generation model using a transformer architecture on a specialized dataset with no pretraining. Additionally, we propose a novel decoding method to enhance coherence and meaningfulness in the generated poetry, effectively managing the tradeoff between diversity and quality. Furthermore, the results of our training approach and the proposed decoding method are evaluated through comprehensive set of automatic and human evaluations and showed its superior capability to generate coherent and meaningful poetry in compare to other decoding methods and an existing Persian large language model (LLM).

Chinese couplet is a special form of poetry composed of complex syntax with ancient Chinese language. Due to the complexity of semantic and grammatical rules, creation of a suitable couplet is a formidable challenge. This paper presents a transformer-based sequence-to-sequence couplet generation model. With the utilization of AnchiBERT, the model is able to capture ancient Chinese language understanding. Moreover, we evaluate the Glyph, PinYin and Part-of-Speech tagging on the couplet grammatical rules to further improve the model.

Rare is the poet who has failed to tackle the glory of trees, whether it's Joyce Kilmer ("I think that I shall never see / A poem lovely as a tree") or Philip Larkin ("the unresting castles thresh / In fullgrown thickness every May"). Now an artificial intelligence trained by experts on more than half a million lines of poetry has had a stab, coming up with the almost-comprehensible image of a "box of light that had been a tree". The algorithm, which those behind it believe is the best attempt to date at training an artificial intelligence to write poetry, was fed lines from more than 100 British contemporary poets as inspiration, learning from the style of poets such as Simon Armitage and Alice Oswald. It was then given "seed words", from which it would generate couplets based on its understanding of what poetry was. Experts from the Poetry Society, Poetry Archive and Scottish Poetry Library then filtered through tens of thousands of couplets to highlight what did, and didn't, work.

The sample-inefficiency problem in Artificial Intelligence refers to the inability of current Deep Reinforcement Learning models to optimize action policies within a small number of episodes. Recent studies have tried to overcome this limitation by adding memory systems and architectural biases to improve learning speed, such as in Episodic Reinforcement Learning. However, despite achieving incremental improvements, their performance is still not comparable to how humans learn behavioral policies. In this paper, we capitalize on the design principles of the Distributed Adaptive Control (DAC) theory of mind and brain to build a novel cognitive architecture (DAC-ML) that, by incorporating a hippocampus-inspired sequential memory system, can rapidly converge to effective action policies that maximize reward acquisition in a challenging foraging task.

We present a simple yet effective method for generating high quality classical Chinese poetry with Generative Pre-trained Language Model (GPT). The method adopts a simple GPT model, without using any human crafted rules or features, or designing any additional neural components. While the proposed model learns to generate various forms of classical Chinese poems, including Jueju, L\"{u}shi, various Cipai and Couples, the generated poems are of very high quality. We also propose and implement a method to fine-tune the model to generate acrostic poetry. To the best of our knowledge, this is the first to employ GPT in developing a poetry generation system. We will release an online demonstration system in the near future to show the generation capability of the proposed method for classical Chinese poetry.

AI or not AI: that is the question. Computer scientists in Australia teamed up with an expert in the University of Toronto's department of English to design an algorithm that writes poetry following the rules of rhyme and metre. To test their results, the researchers asked people online to distinguish between human- and bot-written verses. The best version of the algorithm fooled people nearly 50 per cent of the time. In some ways, the computer's verses were better than Shakespeare's.