Mitigating style-image hallucination in large vision language models
LLMs are widely applied across various domains, yet a significant challenge remains—their performance deteriorates sharply in out-of-domain scenarios, often leading to increased hallucinations. Despite its importance, this phenomenon has received limited attention in academic research. To address th...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2025
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| Online Access: | https://hdl.handle.net/10356/182918 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | LLMs are widely applied across various domains, yet a significant challenge remains—their performance deteriorates sharply in out-of-domain scenarios, often leading to increased hallucinations. Despite its importance, this phenomenon has received limited attention in academic research. To address this, we first construct a benchmark dataset using style transfer techniques and employ it to evaluate the out-of-domain performance of several popular large-scale models. Building upon these findings, we introduce CopeCap, a lightweight image captioning model that leverages collaborative prompting to achieve strong out-of-domain performance without requiring additional training. |
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