Hallucinations in Scholarly LLMs

A Conceptual Overview and Practical Implications

Abstract

The issue of large language models (LLMs) is gradually infiltrating the academic workflow, but it also presents one significant problem: hallucination. The hallucinations involve invented research results, ideas of fabricated reference, and misinterpreted inferences that destroy the credibility and dependability of scholarly writing. In the present paper, the concept of hallucinations as the aspect of scholarly communication is discussed, the major types of hallucinations are revealed, and the causes along with effects of hallucinations are discussed. It also examines pragmatic mitigation measures, such as retrieval-augmented generation (RAG) of factual grounding, citation-verification, and neurosymbolic strategies of structured fact-checking. The paper additionally emphasizes the significance of human-AI partnership in the process of creating scholarly tools to make the use of AI in research responsible and verifiable. The paper seeks to create awareness and offer guidance to the creation of reliable AI systems to be used in scholarly contexts by synthesizing risks, opportunities, and available mitigation measures to such systems. Instead of presenting a comprehensive technical structure, the work provides an overview of the conceptual description which may be used to design more reliable, transparent, and fact-driven AI-assisted research tools.