通过与事实和幻觉提示进行对比解码来改善事实性
Bojie Lv1, Ao Feng1, Chenlong Xie1
1School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
我们为大型语言模型 (LLM) 开发了一种新的解码方法,该方法使用事实和幻觉提示来提高准确性. 这种方法显著提高了事实正确性,而不需要额外的培训,使LLMs更可靠.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 具有先进的功能,但容易产生不准确或无关的输出,这种现象被称为幻觉.
- 确保LLM生成内容的事实准确性对于可靠的应用程序至关重要.
研究的目的:
- 引入一种新的解码方法,即用事实和幻觉提示 (DFHP) 解码,以减轻LLMs中的幻觉.
- 为了提高LLMs的事实准确性,而不需要再培训.
主要方法:
- 该DFHP方法采用对比解码来区分实际和幻觉提示之间的输出概率.
- 这种方法评估了LLM在多选项和文本生成任务上的表现.
主要成果:
- 通过DFHP,在各种模型大小中显著提高了LLM的实际准确性.
- 在TruthfulQA数据集上,DFHP对7B,13B,30B和65B版本的LLaMA模型的事实准确性平均提高了6.4%.
- 该方法在不需要额外的模型培训的情况下证明了有效性.
结论:
- DFHP解码方法在LLM事实准确性方面提供了显著的改进.
- 它的高可靠性使其适用于医疗诊断和法律案例分析等关键应用.
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