Multimodal KB-VQA exhibits a primacy bias where gold passages at prompt start outperform those at the end by 16-26 points, flipping the text-only lost-in-the-middle pattern.
Order matters: Exploring or- der sensitivity in multimodal large language models
5 Pith papers cite this work. Polarity classification is still indexing.
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DGAO uses reinforcement learning to optimize LLMs for both accuracy and order stability by balancing intra-group accuracy advantages and inter-group stability advantages.
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.
LLMs succeed at graph isomorphism detection but fail to recognize isomorphic graphs under node label permutation, indicating pattern exploitation over topological understanding.
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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Lost at the End: Primacy Bias in Multimodal Retrieval-Augmented Question Answering
Multimodal KB-VQA exhibits a primacy bias where gold passages at prompt start outperform those at the end by 16-26 points, flipping the text-only lost-in-the-middle pattern.
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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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Detecting Differences Is Not Understanding Structure: Large Language Models Fail at Graph Isomorphism
LLMs succeed at graph isomorphism detection but fail to recognize isomorphic graphs under node label permutation, indicating pattern exploitation over topological understanding.
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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.