# Detection and Interpretability Analysis of Quotation Errors by Large Language Models

arXiv: https://arxiv.org/abs/2606.08589 · HTML: https://mlsanity.com/paper/2606.08589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Fact or Fiction: Verifying Scientific Claims](https://mlsanity.com/paper/2004.14974/) (2020)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Scientific Claim Verification with VERT5ERINI](https://mlsanity.com/paper/2010.11930/) (2020)
- [TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation](https://mlsanity.com/paper/2407.10114/) (2024)
- [A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification](https://mlsanity.com/paper/2012.14500/) (2020)
- [Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification](https://mlsanity.com/paper/2110.15116/) (2021)
- [A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing](https://mlsanity.com/paper/2412.16620/) (2024)
- [A review of the literature on citation impact indicators](https://mlsanity.com/paper/1507.02099/) (2015)
- [Comparing Knowledge Sources for Open-Domain Scientific Claim Verification](https://mlsanity.com/paper/2402.02844/) (2024)
- [Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences](https://mlsanity.com/paper/2309.06578/) (2023)
- [Detecting Reference Errors in Scientific Literature with Large Language Models](https://mlsanity.com/paper/2411.06101/) (2024)
- [Citation Structural Diversity: A Novel and Concise Metric Combining Structure and Semantics for Literature Evaluation](https://mlsanity.com/paper/2501.02429/) (2025)
- [References of References: How Far is the Knowledge Ancestry](https://mlsanity.com/paper/2101.08577/) (2021)

## Abstract (arXiv)

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.