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New Research Highlights BibTeX Citation Errors in LLMs, Proposes Fix

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv details significant BibTeX citation errors generated by large language models, even when equipped with web search capabilities. The study found that models like GPT-5, Claude Sonnet-4.6, and Gemini-3 Flash struggle with accuracy, particularly for recent or less-cited papers, often substituting entire entries or making isolated field errors. To address this, the researchers developed 'clibib,' an open-source tool that, when integrated into a two-stage process, significantly improves citation accuracy and reduces regression rates compared to single-stage tool loops. AI

IMPACT Highlights the need for improved citation accuracy in AI-powered scientific workflows and introduces a potential solution.

RANK_REASON The cluster contains a research paper detailing an evaluation and mitigation of errors in LLM-generated BibTeX citations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

New Research Highlights BibTeX Citation Errors in LLMs, Proposes Fix

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The cluster contains a research paper detailing an evaluation and mitigation of errors in LLM-generated BibTeX citations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Delip Rao, Chris Callison-Burch ·

    BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation

    arXiv:2604.03159v2 Announce Type: replace-cross Abstract: Large language models with web search are increasingly used in scientific publishing agents, yet they produce BibTeX entries with pervasive field-level errors stemming from omission, partial corruption, substitution, and h…

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