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Kim Yeonju

2 accepted papers

2024

CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models

NeurIPS 2024poster

Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation. However, alongside these advancements, the issue of hallucinations has emerged as a significant challenge, producing erroneous responses that are unrelate…

Cited by 52SourcePDFScholar
2024

What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models

EMNLP 2024finding

This paper presents a way of enhancing the reliability of Large Multi-modal Models (LMMs) in addressing hallucination, where the models generate cross-modal inconsistent responses. Without additional training, we propose Counterfactual Inception, a novel method that implants counterfactual thinking…