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Found 3 papers, 2 papers with code
Strengthening Multimodal Large Language Model with Bootstrapped Preference Optimization
To mitigate this issue, we propose Bootstrapped Preference Optimization (BPO), which conducts preference learning with datasets containing negative responses bootstrapped from the model itself.
Ranked #31 on
Visual Question Answering
on MM-Vet
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs
In this paper, we identify a typical class of inputs that baffles MLLMs, which consist of images that are highly relevant but inconsistent with answers, causing MLLMs to suffer from hallucination.
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.
