Detection and Interpretability Analysis of Quotation Errors by Large Language Models

• URL: http://arxiv.org/abs/2606.08589v1
• Date: Sun, 07 Jun 2026 12:01:48 GMT
• Title: Detection and Interpretability Analysis of Quotation Errors by Large Language Models
• Abstract summary: This paper proposes the task of 'automated detection of quotation errors'<n>The fine-tuning approach for large language models (LLMs) is applied to the task of automated detection of quotation errors.<n> interpretability analysis is conducted on the model's output results.
• Score: 3.806421007129287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose - Quotation error refers to the inconsistency between cited information and its original source. This phenomenon leads to a series of negative impacts, such as misinterpretation of the original research, undermining the academic community's collective understanding of relevant issues, and weakening the accuracy and fairness of the citation-based academic evaluation system. Existing studies have shown that quotation error is prevalent in the academic community; moreover, manual verification of quotation error is not only labor-intensive but also inefficient. Therefore, this paper proposes the task of 'automated detection of quotation errors'. Methodology - Adopting a large language model (LLM)-based approach, this paper improves detection performance from two aspects on the basis of existing research: first, employ the fine-tuning approach for LLMs to detect quotation errors; second, incorporating full-text data of the cited literature into dataset construction, and exploring the optimal scheme for building such datasets by comparing three types of full-text integration methods. Based on this, this paper further uses the TokenSHAP tool to conduct interpretability experimental analysis on the model's prediction results. Findings - The fine-tuning approach for LLMs has improved the performance in detecting quotation errors. Among the different methods for incorporating full-text information, the approach based on using the source abstract yielded the best performance. Originality - The fine-tuning approach for large language models (LLMs) is applied to the task of automated detection of quotation errors, and interpretability analysis is conducted on the model's output results.

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