This study evaluated the ability of several Large Language Models (LLMs) to pastiche the literary style of the Romanian 20th century author Mateiu Caragiale, by continuing one of his novels left unfinished upon his death. We assembled a database of novels consisting of six texts by Mateiu Caragiale, including his unfinished one, six texts by Radu Albala, including a continuation of Mateiu’s novel, and six LLM generated novels that try to pastiche it. We compared the LLM generated texts with the continuation by Radu Albala, using various methods. We automatically evaluated the pastiches by standard metrics such as ROUGE, BLEU, and METEOR. We performed stylometric analysis, clustering, and authorship attribution, and a manual analysis. Both computational and manual analysis of the pastiches indicated that LLMs are able to produce fairly qualitative pastiches, without matching the professional writer performance. The study also showed that ML techniques outperformed the more recent DL ones in both clusterization and authorship attribution tasks, probably because the dataset consists of only a few literary archaic texts in Romanian. In addition, linguistically informed features were shown to be competitive compared to automatically extracted features.

Since the advent of Large Language Models (LLMs), the interest and need for a better understanding of artificial creativity has increased.This paper aims to design and administer an integrated language creativity test, including multiple tasks and criteria, targeting both LLMs and humans, for a direct comparison. Language creativity refers to how one uses natural language in novel and unusual ways, by bending lexico-grammatical and semantic norms by using literary devices or by creating new words. The results show a slightly better performance of LLMs compared to humans. We analyzed the responses dataset with computational methods like sentiment analysis, clusterization, and binary classification, for a more in-depth understanding. Also, we manually inspected a part of the answers, which revealed that the LLMs mastered figurative speech, while humans responded more pragmatically.

The following paper presents the outcomes of a collaborative experiment on human evaluation from the ReproNLP 2024 shared task, track B, part of the ReproHum project. For this paper, we evaluated a QAG (question-answer generation) system centered on English children’s storybooks that was presented in a previous research, by using human evaluators for the study. The system generated relevant QA (Question-Answer) pairs based on a dataset with storybooks for early education (kindergarten up to middle school) called FairytaleQA. In the framework of the ReproHum project, we first outline the previous paper and the reproduction strategy that has been decided upon. The complete setup of the first human evaluation is then described, along with the modifications required to replicate it. We also add other relevant related works on this subject. In conclusion, we juxtapose the replication outcomes with those documented in the cited publication. Additionally, we explore the general features of this endeavor as well as its shortcomings.