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LLMs fine-tuned to detect academic quotation errors

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a new methodology for detecting errors in LLM-generated academic content. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs fine-tuned to detect academic quotation errors

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The cluster contains an academic paper detailing a new methodology for detecting errors in LLM-generated academic content. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chengzhi Zhang ·

    Detection and Interpretability Analysis of Quotation Errors by Large Language Models

    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 rel…

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