DGAO uses reinforcement learning to optimize LLMs for both accuracy and order stability by balancing intra-group accuracy advantages and inter-group stability advantages.
arXiv preprint arXiv:2410.16983 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A training-free attention-guided debiasing framework mitigates position bias in MLLM multi-image retrieval by exploiting the observed mismatch between biased logits and aligned attention maps, yielding over 40% accuracy gains on MS-COCO benchmarks.
MetaRA applies metamorphic testing to VQA tasks and shows that MLLM models exhibit sensitivity to linguistic perturbations and superficial visual cues not detected by conventional accuracy benchmarks.
citing papers explorer
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Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
DGAO uses reinforcement learning to optimize LLMs for both accuracy and order stability by balancing intra-group accuracy advantages and inter-group stability advantages.
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Logit-Attention Divergence: Mitigating Position Bias in Multi-Image Retrieval via Attention-Guided Calibration
A training-free attention-guided debiasing framework mitigates position bias in MLLM multi-image retrieval by exploiting the observed mismatch between biased logits and aligned attention maps, yielding over 40% accuracy gains on MS-COCO benchmarks.
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MetaRA: Metamorphic Robustness Assessment for Multimodal Large Language Model-based Visual Question Answering Systems
MetaRA applies metamorphic testing to VQA tasks and shows that MLLM models exhibit sensitivity to linguistic perturbations and superficial visual cues not detected by conventional accuracy benchmarks.