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Physical Sciences › Computer Science › Artificial Intelligence
A comprehensive taxonomy of hallucinations in Large Language Models
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Identified Weaknesses
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Paper Summary
Paperzilla title
LLMs Hallucinate: It's Not a Bug, It's a Feature!
This paper presents a comprehensive taxonomy of hallucinations in Large Language Models (LLMs), categorizing them based on their relationship to input context and factual accuracy. It explores various types of hallucinations, their potential causes stemming from data limitations and model architecture, and discusses mitigation strategies like tool augmentation and retrieval methods. The authors also highlight the inherent inevitability of some level of hallucination in current LLMs, emphasizing the need for robust detection and ongoing human oversight.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Lack of Novel Empirical Research
The paper primarily relies on theoretical frameworks and existing literature reviews, lacking novel empirical research or experimental validation. This makes it difficult to assess the practical effectiveness of proposed mitigation strategies or quantify their impact on hallucination rates across diverse real-world applications.
Lack of Standardized Hallucination Definitions
While the taxonomy of hallucination types provides a useful conceptual framework, it also highlights the ongoing challenge of inconsistent definitions and categorizations in the field. This lack of standardization makes comparing results across different studies and developing unified evaluation metrics more difficult.
Lack of Quantitative Estimates for Mitigation Effectiveness
The paper acknowledges the theoretical inevitability of hallucinations in computable LLMs but does not offer concrete quantitative estimations of the achievable reduction in hallucination rates through proposed mitigation strategies. This makes assessing the potential effectiveness and practical impact of these strategies challenging.
Rating Explanation
This comprehensive review provides a valuable taxonomy of LLM hallucinations and explores underlying causes and mitigation strategies, offering a solid theoretical foundation. Despite lacking novel empirical research, its systematic approach and detailed analysis of existing literature justify a strong rating. The discussed limitations regarding standardization and quantifying mitigation effectiveness prevent a top rating.
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File Information
Original Title:
A comprehensive taxonomy of hallucinations in Large Language Models
File Name:
2508.01781v1.pdf
[download]
File Size:
3.27 MB
Uploaded:
August 05, 2025 at 03:28 PM
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