Work

Are you hallucinated? Insights into large language models

Year: 2025

Type: article

Abstract: The work presented in the paper challenges the common view of hallucination as a flaw in Large Language Models (LLMs), proposing instead that it is an inherent feature of their architecture. It explores the relationship between “creativity,” “generalization,” and “hallucination,” showing that they result from the transformer model’s high-dimensional vector blending. Using Gemini 2.0 and 2.5 models, the authors demonstrate a “Self-Correction Blind Spot,” where LLMs can recognize errors but lack the reasoning pathways to correct them. The paper introduces the “Generalization-Hallucination Trade-off,” suggesting that minimizing hallucination would impede generalization. The research advocates for managing hallucination with external verification rather than eliminating it.

Source: ICT Express

DOI: 10.1016/j.icte.2025.12.011

Authors: Sarvagya Singh, Rahul Saha, Gulshan Kumar, Anand Nayyar, Tae-Kyung Kim

Institutions: Lovely Professional University, Duy Tan University, Chungbuk National University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: Ministry of Education, National Research Foundation of Korea