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Visually Dehallucinative Instruction Generation: Know What You Don't Know

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arxiv 2402.09717 v1 pith:FHATJRU6 submitted 2024-02-15 cs.CV

Sungguk Cha , Jusung Lee , Younghyun Lee , Cheoljong Yang This is my paper · ORCID
classification cs.CV
keywords hallucinationknowinstructiondehallucinativeeffectivelygenerationvisualvisually
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"When did the emperor Napoleon invented iPhone?" Such hallucination-inducing question is well known challenge in generative language modeling. In this study, we present an innovative concept of visual hallucination, referred to as "I Know (IK)" hallucination, to address scenarios where "I Don't Know" is the desired response. To effectively tackle this issue, we propose the VQAv2-IDK benchmark, the subset of VQAv2 comprising unanswerable image-question pairs as determined by human annotators. Stepping further, we present the visually dehallucinative instruction generation method for IK hallucination and introduce the IDK-Instructions visual instruction database. Our experiments show that current methods struggle with IK hallucination. Yet, our approach effectively reduces these hallucinations, proving its versatility across different frameworks and datasets.

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  1. PRISM: Priority-aware Rubric Internalization via Structured Multimodal Data Synthesis

    cs.LG 2026-08 conditional novelty 7.0 of 10

    PRISM trains MLLMs to act as rubric executors by synthesizing typed, prioritized rules and verification traces, lifting Qwen3-VL-4B from 9.5% to 30.1% Strict accuracy on the authors' PRISM-Eval benchmark.

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