Comprehensive Evaluation of AI Hallucination and Novel UV-Oriented Framework toward Safe and Trustworthy AI
this work, we present a comprehensive evaluation of hallucination phenomena in LLMs and multimodal systems. First, we propose a structured taxonomy encompassing factuality-based, faithfulness-based, logical-based, and emergent hybrid forms, extending to multimodal-specific risks such as cross-modal inconsistencies, visual overinterpretation, and modality dominance effects. Second, we conduct a mechanistic analysis that traces hallucinations across the full model lifecycle—data-level origins (knowledge gaps, misinformation, annotation noise), training-induced mechanisms (distributional mimicry, reward bias, alignment forgetting), and inference-time vulnerabilities (confidence miscalibration, decoding failures, prompt-induced errors). This perspective reveals how independent mechanisms interact to produce cascade effects, amplifying initial flaws into elaborate but unreliable narratives. Third, we critically assess existing detection and evaluation approaches, highlighting limitations of current benchmarks, taxonomic ambiguities, and the lack of mechanism-aware evaluation protocols. We argue that future evaluation must integrate both surface-level manifestations and their generative causes to achieve more robust measurement.Beyond evaluation, we survey mitigation strategies organized into mechanism-based, phase-based, and hybrid approaches. These range from lightweight prompt engineering and decoding constraints to resource-augmented methods such as retrieval-augmented generation and knowledge graph integration, as well as higher-level frameworks for controllability and uncertainty calibration. We analyze the trade-offs among effectiveness, scalability, interpretability, and creative freedom, emphasizing the importance of context-specific tolerances: hallucinations that are unacceptable in medicine or finance may be tolerable, or even beneficial, in creative applications.Building on these insights, we propose a novel UV-oriented framework for safe and trustworthy AI, inspired by the Universal Village vision of harmonizing human, technological, and environmental systems. In this framework, hallucination is conceptualized not only as an isolated model error but as a systemic vulnerability in information flow and decision-making loops. We design a multi-level dynamic system integrating sensing, communication, decision-making, action, and evaluation, supported by closed feedback loops and user-specific hallucination tolerance levels. This architecture enables adaptive mitigation through mechanism-informed strategies, retrieval and verification integration, and consensus mechanisms, ensuring resilience across diverse application contexts.Our contributions are threefold: (1) we present the most comprehensive taxonomy of hallucination to date, linking manifestations with mechanistic drivers; (2) we unify detection, evaluation, and mitigation strategies into a coherent survey that highlights both current progress and pressing gaps; and (3) we introduce a UV-oriented framework that reframes hallucination control as part of a broader, feedback-driven ecosystem for reliable AI. We conclude by outlining open challenges, including classification ambiguities, multimodal alignment risks, deployment-specific vulnerabilities, and the need to reconcile reliability with creativity. Addressing these challenges is essential to advancing from powerful yet fallible generative models toward AI systems that are not only safe and responsible but also aligned with human values and societal needs.
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Comprehensive Evaluation of AI Hallucination and Novel UV-Oriented Framework toward Safe and Trustworthy AI
Semantic Scholar · 2024
Abstract
this work, we present a comprehensive evaluation of hallucination phenomena in LLMs and multimodal systems. First, we propose a structured taxonomy encompassing factuality-based, faithfulness-based, logical-based, and emergent hybrid forms, extending to multimodal-specific risks such as cross-modal inconsistencies, visual overinterpretation, and modality dominance effects. Second, we conduct a mechanistic analysis that traces hallucinations across the full model lifecycle—data-level origins (knowledge gaps, misinformation, annotation noise), training-induced mechanisms (distributional mimicry, reward bias, alignment forgetting), and inference-time vulnerabilities (confidence miscalibration, decoding failures, prompt-induced errors). This perspective reveals how independent mechanisms interact to produce cascade effects, amplifying initial flaws into elaborate but unreliable narratives. Third, we critically assess existing detection and evaluation approaches, highlighting limitations of current benchmarks, taxonomic ambiguities, and the lack of mechanism-aware evaluation protocols. We argue that future evaluation must integrate both surface-level manifestations and their generative causes to achieve more robust measurement.Beyond evaluation, we survey mitigation strategies organized into mechanism-based, phase-based, and hybrid approaches. These range from lightweight prompt engineering and decoding constraints to resource-augmented methods such as retrieval-augmented generation and knowledge graph integration, as well as higher-level frameworks for controllability and uncertainty calibration. We analyze the trade-offs among effectiveness, scalability, interpretability, and creative freedom, emphasizing the importance of context-specific tolerances: hallucinations that are unacceptable in medicine or finance may be tolerable, or even beneficial, in creative applications.Building on these insights, we propose a novel UV-oriented framework for safe and trustworthy AI, inspired by the Universal Village vision of harmonizing human, technological, and environmental systems. In this framework, hallucination is conceptualized not only as an isolated model error but as a systemic vulnerability in information flow and decision-making loops. We design a multi-level dynamic system integrating sensing, communication, decision-making, action, and evaluation, supported by closed feedback loops and user-specific hallucination tolerance levels. This architecture enables adaptive mitigation through mechanism-informed strategies, retrieval and verification integration, and consensus mechanisms, ensuring resilience across diverse application contexts.Our contributions are threefold: (1) we present the most comprehensive taxonomy of hallucination to date, linking manifestations with mechanistic drivers; (2) we unify detection, evaluation, and mitigation strategies into a coherent survey that highlights both current progress and pressing gaps; and (3) we introduce a UV-oriented framework that reframes hallucination control as part of a broader, feedback-driven ecosystem for reliable AI. We conclude by outlining open challenges, including classification ambiguities, multimodal alignment risks, deployment-specific vulnerabilities, and the need to reconcile reliability with creativity. Addressing these challenges is essential to advancing from powerful yet fallible generative models toward AI systems that are not only safe and responsible but also aligned with human values and societal needs.