What if...?: Counterfactual Inception to Mitigate Hallucination Effects
  in Large Multimodal Models
Junho Kim Yeon Ju Kim Yong Man Ro
This paper presents a way of enhancing the reliability of Large Multimodal
Models (LMMs) in addressing hallucination effects, where models generate
incorrect or unrelated responses. Without additional instruction tuning
paradigm, we introduce Counterfactual Inception, a novel method that implants
counterfactual thoughts into LMMs using carefully chosen, misaligned
counterfactual keywords. This method is grounded in the concept of
counterfactual thinking, a cognitive process where humans consider alternative
realities and outcomes. By applying this human-like reasoning mechanism to
LMMs, we aim to reduce hallucination effects and improve the models'
trustworthiness. We also propose Dual-modality Verification Process (DVP), a
rigorous framework for selecting optimal counterfactual keywords to trigger
counterfactual thinking into LMMs, concurrently considering visual and
linguistic context. Our extensive experiments across various LMMs, including
both open-source and proprietary models, corroborate that our method
significantly mitigates hallucination phenomena across different datasets.