What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models (2024)

Abstract: 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 into LMMs using self-generated counterfactual keywords. Our method is grounded in the concept of counterfactual thinking, a cognitive process where human considers alternative realities, enabling more extensive context exploration. Bridging the human cognition mechanism into LMMs, we aim for the models to engage with and generate responses that span a wider contextual scene understanding, mitigating hallucinatory outputs. We further introduce Plausibility Verification Process (PVP), a simple yet robust keyword constraint that effectively filters out sub-optimal keywords to enable the consistent triggering of counterfactual thinking in the model responses. Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination and helps to broaden contextual understanding based on true visual clues.

## introduction after witnessing the great success of large language models (llms) products, such as chat-gpt @xcite and gemini @xcite , the emergence of large multi-modal models (lmms) naturally followed as the next step towards a unified, general-purpose ai system @xcite @xcite . in the vision research area, various works @xcite @xcite have actively resorted llms into the vision models due to their remarkable capability of off-the-shelf text generation. especially when it large multimodal model 𝓞𝓞bject baseline response (w/o counterfactual prompt): the cat in the middle of the image opens its mouth. ## conclusion in this work, we propose a novel method of reducing hallucination in lmms, counterfactual inception. by integrating counterfactual thinking to the models through self-generated keywords, our approach improves the reliability of model responses. the introduction of plausibility verification process (pvp) further ensures the precision of selecting counterfactual keywords to implant counterfactual thinking. our extensive analyses across various models and benchmarks corroborate that our approach can effectively trigger exceptional thought to the models without additional training and mitigate hallucination in their responses.