Detection and Interpretability Analysis of Quotation Errors by Large Language Models
Researchers have developed a new method for automatically detecting quotation errors in academic papers using fine-tuned large language models. This approach aims to improve the accuracy and efficiency of identifying inconsistencies between cited information and its original source. The study found that incorporating the full text of cited literature, particularly the abstract, significantly enhanced detection performance. Additionally, the researchers utilized the TokenSHAP tool to analyze the interpretability of the model's predictions. AI
IMPACT Improves the reliability of academic research and citation integrity by detecting LLM-introduced errors.