مروری سیستماتیک بر قابلیت اعتماد، توهم و ایمنی در مدلهای بزرگ بینایی-زبان
حانیه نادری
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دانشکده مهندسی سامانههای هوشمند، دانشکدگان علوم و فناوریهای میانرشتهای، دانشگاه تهران، تهران، ایران
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دانشکده مهندسی سامانههای هوشمند، دانشکدگان علوم و فناوریهای میانرشتهای، دانشگاه تهران، تهران، ایران
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کلید واژه: مدلهای بینایی-زبان, قابلیت اعتماد, توهم, ایمنی چندوجهی, جیلبریک بصری,
چکیده مقاله :
مدلهای بزرگ بینایی-زبان با وجود پیشرفتهای چشمگیر در وظایف چندوجهی، همچنان با دو چالش بنیادین قابلیت اعتماد روبهرو هستند: تولید محتوای توهمی ناسازگار با شواهد بصری، و آسیبپذیری در برابر حملات خصمانه که سازوکارهای ایمنی را دور میزنند. این مرور سیستماتیک با پیروی از چارچوب گزارشدهی پریزما ۲۰۲۰ و بررسی ۳۱ مطالعه منتخب از سالهای ۲۰۲۳ تا ۲۰۲۵، به بررسی ریشههای فنی توهم در سطوح مختلف (شیء، ویژگی، رابطه و روایت)، سازوکارهای جیلبریک بصری، و راهبردهای کاهش در زمان آموزش و استنتاج میپردازد. یافتههای این مطالعه نشان میدهد که اتکای بیش از حد به پیشینهای زبانی، ریشه مشترک بسیاری از خطاهای توهمی و شکستهای ایمنی است و همترازی صرفاً متنی برای سیستمهای چندوجهی کافی نیست. همچنین، شکاف معناداری میان توانمندیهای ارزیابی فعلی و پیچیدگی واقعی آسیبپذیریها وجود دارد که مانع سنجش جامع قابلیت اعتماد میشود. این مرور با ارائه تاکسونومی یکپارچه از آسیبپذیریها و نقشه راه پژوهشی، چارچوبی برای توسعه مدلهای قابل اعتمادتر فراهم میآورد.
چکیده انگلیسی :
Large Vision–Language Models (LVLMs), despite their remarkable progress in multimodal tasks, continue to face two fundamental reliability challenges: the generation of hallucinatory content inconsistent with visual evidence, and vulnerability to adversarial attacks that bypass safety mechanisms. This systematic review, conducted in accordance with the PRISMA 2020 reporting framework and based on 31 selected studies from 2023 to 2025, investigates the technical roots of hallucination across multiple levels (object, attribute, relation, and narrative), visual jailbreak mechanisms, and mitigation strategies at both training and inference stages.
The findings indicate that over-reliance on linguistic priors is a common underlying factor behind many hallucination errors and safety failures, and that purely text-based alignment is insufficient for multimodal systems. Furthermore, a significant gap exists between current evaluation capabilities and the real-world complexity of vulnerabilities, hindering comprehensive reliability assessment.
This review provides an integrated taxonomy of vulnerabilities and outlines a research roadmap, offering a structured foundation for the development of more reliable vision–language models.
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