# Can Artificial Intelligence Write Like [PERSON]? An Evaluation Protocol for Spanish Microfiction
## 1 Introduction  
Technological progress in artificial intelligence (AI) has led to systems capable of advanced reasoning [1, 2, 3], multimodal understanding [3] and creative writing [4]. Additionally, improvements in knowledge distillation [5] and reductions in inference-time computational costs [3, 6] suggest that generative AI systems are becoming more accessible and affordable, with some scientific studies even suggesting that AI-generated texts may surpass human-written ones in literary quality [7, 8]. However, the assessment of literary quality has always been a subjective matter. A review of current methods for evaluating automatically generated fiction [9] reveals that literary criteria are seldom considered. In particular, the concept of reception [10, 11] plays a crucial role. As the above studies indicate, the reception of literary texts is significantly influenced by readers' experiences and literary expertise. Consequently, an evaluation framework grounded in literary theory is essential for effectively comparing human-authored texts with those produced by generative models. Moreover, beyond direct human-AI comparison, it becomes relevant to assess the aesthetic quality of AI-generated fiction through a literary evaluation protocol. Currentevaluation methods mostly come from the fields of natural language processing and machine translation, and exhibit significant limitations when applied to literary texts. These methods rely heavily on quantitative metrics such as BLEU, ROUGE, and perplexity, which fail to capture nuanced aspects of literary language such as metaphor, symbolism, and stylistic creativity. These metrics prioritize surface-level similarity over deeper semantic and aesthetic qualities, leading to inadequate assessments of text richness. While AI mechanisms can generate consistent and coherent fiction, little attention has been paid to producing texts with literary value. This raises a fundamental question: what makes literature literature? This paper introduces the _Grading AI and Human Microfiction Evaluation System_, or GrAlmes. The GrAlmes evaluation protocol is named after [PERSON] (born 1922), an American linguist known for his work in discourse analysis, computational linguistics, and indigenous languages of America. He developed the first automatic story generator in the early 1960s ([PERSON] _et al._, 2012), a pioneering system that used [PERSON]'s analysis of Russian folk stories ([PERSON] _et al._, 2013) as a grammar for story generation. Our novel evaluation protocol is specifically designed to assess the literary value of microfiction. GrAlmes incorporates literary criteria into the assessment process, aiming to evaluate the literary quality of microfiction generated by AI or created by human authors with or without AI assistance. GrAlmes' reception situation is inspired by the editorial process used to accept or reject stories submitted to the publishing industry ([PERSON] _et al._, 2013). We chose to work with microfiction (see Section 2.1) as a model of literary narrative due to the limitations of language models in generating short narratives and because brevity facilitates the evaluation process. GrAlmes consists of a questionnaire with fifteen questions designed to assess literary originality, the impact of microfiction on its readers, and to some extent its commercial potential. The protocol's development involved an initial validation phase during which a group of experts (literary scholars holding a PhD and an academic position) assessed human-written microfiction. Subsequently, GrAlmes was applied to AI-generated microfiction, with evaluations conducted by a community of reading and literature enthusiasts. In both experiments, it was necessary to take into consideration that human evaluators base their assessments on their imagination and prior knowledge, often assigning higher ratings to stories they find more familiar ([PERSON] _et al._, 2013).