Fixing Reference Hallucinations of LLMs

In October and November 2024, using popular LLMs like OpenAI ChatGPT (4 and below), Azure OpenAI and its Copilot instantiations, Google Gemini and GenAI LLM tuned for scientific papers like Zendy, asking a question and references produces with every LLM fake references, well constructed, but with different titles or authors than the web or journal reference actually associated to the citation, or sometimes totally invented. Prompting to ensure that the reference exists and is correct may help for some, but in general it does not. Others have reported similar issues when using these LLM/GenAI services to produce legal briefs, and other legal documents. This paper suggest simple ways to address this, instead of trying to just improve the LLMs and hope hallucinations will be reduced; they won’t, no matter what, they are inherent to LLM. It is very surprising and mindboggling that LLM providers have not been implementing these kind of solutions: just check if the references exist, are correctly cited, and relevant to the paper/context. We also expand the approach with our MultiAI approach to improve on the previous approach, or address other hallucinations; actually eliminating in our tests.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-11-29
DOI
https://doi.org/10.5281/zenodo.14543939
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.24
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article

Fixing Reference Hallucinations of LLMs

2 citations
Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
0.24
article

Fixing Reference Hallucinations of LLMs

article en
2 citations

Abstract

In October and November 2024, using popular LLMs like OpenAI ChatGPT (4 and below), Azure OpenAI and its Copilot instantiations, Google Gemini and GenAI LLM tuned for scientific papers like Zendy, asking a question and references produces with every LLM fake references, well constructed, but with different titles or authors than the web or journal reference actually associated to the citation, or sometimes totally invented. Prompting to ensure that the reference exists and is correct may help for some, but in general it does not. Others have reported similar issues when using these LLM/GenAI services to produce legal briefs, and other legal documents. This paper suggest simple ways to address this, instead of trying to just improve the LLMs and hope hallucinations will be reduced; they won’t, no matter what, they are inherent to LLM. It is very surprising and mindboggling that LLM providers have not been implementing these kind of solutions: just check if the references exist, are correctly cited, and relevant to the paper/context. We also expand the approach with our MultiAI approach to improve on the previous approach, or address other hallucinations; actually eliminating in our tests.

Zenodo (CERN European Organization for Nuclear Research)
Escola Superior de Saúde Egas Moniz (PT)
Peace, Justice and strong institutions
Openalex Percentile: Top 38%
Artificial Intelligence in Healthcare and Education
0.24
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