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Found 2 papers, 1 papers with code
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
In this paper, we propose a targeted instruction data generation framework named DFTG that tailored to the hallucination specificity of different models.
Echoes: Unsupervised Debiasing via Pseudo-bias Labeling in an Echo Chamber
This paper first presents experimental analyses revealing that the existing biased models overfit to bias-conflicting samples in the training data, which negatively impacts the debiasing performance of the target models.
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