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A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models

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arxiv 2405.09589 v4 pith:5AX3TNA7 submitted 2024-05-15 cs.LG cs.AIcs.CLcs.CVcs.SDeess.AS

Pranab Sahoo , Prabhash Meharia , Akash Ghosh , Sriparna Saha , Vinija Jain , Aman Chadha This is my paper
classification cs.LGcs.AIcs.CLcs.CVcs.SDeess.AS
keywords foundationhallucinationmodelsaudioimagevideoacrosscomprehensive
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of FMs brings forth a critical challenge: the potential to generate hallucinated outputs, particularly in high-stakes applications. The tendency of foundation models to produce hallucinated content arguably represents the biggest hindrance to their widespread adoption in real-world scenarios, especially in domains where reliability and accuracy are paramount. This survey paper presents a comprehensive overview of recent developments that aim to identify and mitigate the problem of hallucination in FMs, spanning text, image, video, and audio modalities. By synthesizing recent advancements in detecting and mitigating hallucination across various modalities, the paper aims to provide valuable insights for researchers, developers, and practitioners. Essentially, it establishes a clear framework encompassing definition, taxonomy, and detection strategies for addressing hallucination in multimodal foundation models, laying the foundation for future research in this pivotal area.

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Cited by 4 Pith papers

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  1. Knowing When to Quit: Diagnosing and Training LLMs to Abort Futile Reasoning

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    A reinforcement-learning method that rewards refusal over hallucination cuts LLM 'futile reasoning' from ~66-79% to 1-7% on Countdown while roughly preserving accuracy.

  2. MESH -- Understanding Videos Like Human: Measuring Hallucinations in Large Video Models

    cs.CV 2025-09 conditional novelty 6.0 of 10

    MESH, a three-layer video hallucination benchmark, shows LVMs ace basic objects and coarse traits but slip badly on fine character details and multi-subject actions in longer clips.

  3. Locus: Agentic Predicate Synthesis for Directed Fuzzing

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    Locus uses an LLM agent to synthesize and validate intermediate predicates that make directed fuzzing reach target bug states faster, reporting an average 41.6x speedup across eight fuzzers.

  4. Infogen: Generating Complex Statistical Infographics from Documents

    cs.CL 2025-07 conditional novelty 6.0 of 10

    Infogen generates complex statistical infographics from text via a metadata-then-code pipeline, and the new Infodat benchmark shows it outperforming GPT-4o and fine-tuned open LLMs on the authors' metrics.

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