MRGD guides MLLM decoding with a learned hallucination reward and a detector-based recall reward, allowing users to trade off object precision, recall, and test-time compute while reducing object hallucinations on CHAIR and AMBER.
Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding
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Controlling Multimodal LLMs via Reward-guided Decoding
MRGD guides MLLM decoding with a learned hallucination reward and a detector-based recall reward, allowing users to trade off object precision, recall, and test-time compute while reducing object hallucinations on CHAIR and AMBER.