Applications, Performance, and Research Gaps of Large Language Models in Literary Studies: A Scoping Review

Novel innovations in large language models (LLMs) have demonstrated their ability to generate and analyze literary texts. As a result of intricate semantic layers, metaphors, polyphony, and nonlinear narrative structures present in literary works, their analysis by LLMs demands a deep cognitive and semantic understanding. Hence, it is essential to investigate the present abilities of LLMs to understand complex literary narratives, assess their performance, and spot their shortcomings to deliver a consistent view regarding the upcoming development of technologies in computational literary studies. This study, through a systematic scoping review of data from 48 peer‐reviewed articles from five major scientific databases, assesses the current state of research on LLM performance in the interpretation and production of literary texts. We analyzed the selected studies according to two principal axes: (1) the fields of literary applications of LLMs and their performance evaluation within each domain and (2) current theoretical and technical challenges and limitations. The findings revealed that LLMs have been applied to eight major literary tasks, including comprehensive literary analysis and interpretation, understanding and extracting character relationships and traits, stylistic analysis and authorship attribution, interpretation of metaphors and rhetorical features, evaluation and generation of literary content by LLMs, quotation attribution to fictional characters, literary text summarization, and literary translation. Key challenges and limitations were also identified, including data bias and dependency, human intervention and evaluation, constraints related to text and narrative length, and limitations in deep understanding and reasoning. In addition, we formulated six recommendations for future studies on developing and implementing LLMs in literary studies. This review provides a comprehensive roadmap for future researchers to identify current strengths and weaknesses, address existing gaps, and leverage the strengths in practical applications, such as literary translation.