A Realistic Evaluation of LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3

Large Language Models (LLMs) zero-shot and few-shot performance are subject to memorization and data contamination, complicating the assessment of their validity. In literary tasks, the performance of LLMs is often correlated to the degree of book memorization. In this work, we carry out a realistic evaluation of LLMs for quotation attribution in novels, taking the instruction fined-tuned version of Llama3 as an example. We design a task-specific memorization measure and use it to show that Llama3's ability to perform quotation attribution is positively correlated to the novel degree of memorization. However, Llama3 still performs impressively well on books it has not memorized nor seen. Data and code will be made publicly available.