1. What if...?: Counterfactual Inception to Mitigate Hallucination Effects in Large Multimodal Models
- Author
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Kim, Junho, Kim, Yeon Ju, Ro, Yong Man, Kim, Junho, Kim, Yeon Ju, and Ro, Yong Man
- Abstract
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., Comment: under review, code available: https://github.com/IVY-LVLM/Counterfactual-Inception
- Published
- 2024