What if...?: 反事实关键词思考有助于缓解大型多模态模型中的幻觉问题
What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models
AI总结：
本研究提出无需额外训练的Counterfactual Inception方法，通过自生成反事实关键词结合PVP过滤机制，将反事实思考植入大型多模态模型，显著缓解幻觉并拓宽基于真实视觉线索的上下文理解。
摘要：
本文提出了一种提升大型多模态模型（Large Multi-modal Models, LMMs）应对幻觉问题可靠性的方法，幻觉指模型生成跨模态不一致响应的现象。在无需额外训练的前提下，我们提出了Counterfactual Inception（反事实植入）这一新颖方法，通过模型自生成的反事实关键词将反事实思考植入LMMs中。该方法以反事实思考概念为基础，反事实思考是人类考量替代现实的认知过程，可实现更广泛的上下文探索。我们将人类认知机制与LMMs相结合，旨在让模型参与并生成覆盖更广泛上下文场景理解的响应，从而减少幻觉输出。我们进一步引入了Plausibility Verification Process（PVP，合理性验证流程），这是一种简单却稳健的关键词约束机制，可有效过滤次优关键词，确保在模型响应中持续触发反事实思考。我们在多种LMMs（包括开源模型和专有模型）上开展了综合分析，结果证实反事实思考能显著减少幻觉，并有助于基于真实视觉线索拓宽上下文理解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
