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基于情感轮的幻觉问题缓解的大型语言建模

Zhenyu Wang¹, Jianmin Wang², Zenan Lu³

¹College of Design and Innovation, Tongji University, Shanghai 200092, China.

Neural networks : the official journal of the International Neural Network Society

|August 22, 2025

概括

这项研究引入了情感增强推断 (EAI) 以减少大型语言模型 (LLM) 的幻觉. 这种新方法提高了多式联网产生的事实准确性和情感连贯性.

科学领域:

人工智能
自然语言处理
计算机视觉

背景情况:

大型语言模型 (LLM) 具有先进的生成能力,但患有幻觉,影响可靠性.
幻觉是可信但事实上不正确或不一致的输出,限制了LLM的实用性.

研究的目的:

提出和评估一种新的情感增强推断 (EAI) 方法,以减轻多式LLM生成中的幻觉.
提高LLM输出中的情感连贯性和语义可靠性.

主要方法:

开发了基于情感轮的视觉对比解码和情感文本符号化.
在生成过程中启用感知,调节和重建情绪信号.
在中性,正面和负面情绪背景下对MSCOCO和GQA多式数据集进行测试.

主要成果:

与基线模型相比,EAI提高了准确性,精度,回忆和F1分数的4-8%.
显著抑制幻觉,特别是在负面情绪环境中 (S3).
精度增加了5. 48% (S3与S1) 和2. 23% (S3与S2).

结论:

在多模式生成中,EAI有效抑制幻觉,增强情绪管理和文本连贯性.
这种方法提供了对LLM输出中的情绪状态的洞察.
欧洲人工智能协会有助于开发更加可靠,可控和以人为中心的人工智能系统.

关键词:

情感调整幻觉大型语言模型多式联网发电情感的轮子

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