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Large language models can detect citation errors in scientific papers, even with limited information. This AI advancement aids in ensuring the integrity of scientific literature and accurate information dissemination.

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Area of Science:

  • Artificial Intelligence
  • Scholarly Publishing
  • Scientific Integrity

Background:

  • Reference errors like citation and quotation mistakes are prevalent in scientific publications.
  • These errors can spread misinformation and are challenging to identify manually, threatening scientific literature integrity.
  • Automated detection methods are needed to address these challenges.

Purpose of the Study:

  • To assess the efficacy of large language models (LLMs) in detecting quotation errors within scientific articles.
  • To evaluate LLM performance with varying levels of contextual information through retrieval augmentation.

Main Methods:

  • Development of an expert-annotated dataset comprising statement-reference pairs from journal articles, with a significant biomedical component.
  • Evaluation of OpenAI's GPT family of large language models on this dataset.
  • Testing LLMs in diverse settings, including those with limited reference data.

Main Results:

  • Large language models demonstrated a notable ability to identify erroneous citations.
  • Effective detection was achieved even with restricted contextual information and without model fine-tuning.
  • The study validates the potential of AI in supporting scientific writing and review processes.

Conclusions:

  • Large language models show promise for automatically detecting reference errors in scientific literature.
  • AI tools can assist in maintaining the accuracy and reliability of published research.
  • This research contributes to leveraging AI for enhancing scientific communication and ensuring factual grounding.