Large language modeling of hallucinatory problem mitigation based on the wheel of emotions

Large Language Models (LLMs) have demonstrated remarkable generative capabilities across a wide range of natural language processing tasks. However, the frequent occurrence of hallucinations-outputs that appear plausible but are factually incorrect or logically inconsistent-poses a significant challenge to the reliability and practical utility of these models. This paper proposes a novel Emotion-Augmented Inference (EAI) method based on the Wheel of Emotions, aiming to mitigate hallucinations in multimodal generation tasks involving LLMs. EAI integrates two core mechanisms: visual-contrastive decoding and affective textual symbolization, which jointly enable the perception, regulation, and reconstruction of emotional signals during generation. These mechanisms enhance emotional coherence and semantic reliability in the model's outputs. Experimental results on two multimodal datasets, MSCOCO and GQA, show that EAI achieves improvements of 4%-8 % over baseline models in terms of key metrics such as accuracy, precision, recall, and F1-score. Additionally, under three emotional contexts-neutral (S1), positive (S2), and negative (S3)-EAI demonstrates particularly strong performance in hallucination suppression. In the S3 condition, accuracy improves by 5.48% and 2.23% compared to S1 and S2, respectively. These findings also indicate that EAI enhances the ability to manage emotion and maintain textual coherence. In summary, EAI not only stabilizes hallucination suppression in multimodal generation but also provides a new perspective for interpreting the emotional states embedded in LLM outputs. The proposed method offers a promising direction for building more trustworthy, controllable, and human-centered AI systems.