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LLM Hallucination

Definition
AI-generated

In large language models, hallucination refers to fluent outputs that are false, unsupported, or fabricated relative to the user’s sources—such as invented citations, wrong equations, or allele directions that do not appear in the data.

Topics
LLM and Agents
Synonyms
AI hallucination
Related
Bias Large Language Model (LLM) Preregistration

Why it matters in GWAS

Automated summaries, code, or variant interpretations from LLMs can look authoritative while contradicting GWAS summary statistics or literature; human review, retrieval grounding, and tool-based verification are essential safeguards.

Example usage

"The pipeline applies LLM Hallucination to improve prediction while monitoring generalization on held-out data."

Bias Large Language Model (LLM) Preregistration Prompt Engineering Retrieval-Augmented Generation (RAG)
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Ji Z, et al. (2023). Survey of hallucination in natural language generation. ACM Comput Surv.
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