关于案件类型的不平衡:评估和增强刑事法院的PLM
IEEE transactions on neural networks and learning systems
|November 3, 2025
概括
新的指标通过专注于案件类型来评估刑事法院视图生成 (CCVG),解决传统平均得分的局限性. 这确保了更公平的评估,并确定了法律文本总结模型中的性能不平衡.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 法律与技术 法律与技术
背景情况:
- 刑事法庭观点生成 (CCVG) 任务需要为判决产生简洁,连贯的法律事实摘要.
- 目前的评估指标如红色,蓝色和伯特斯科尔在所有样本中的平均表现,未能考虑不同类型案例之间的差异.
- 这种平均值导致不公平的评估,并忽略了各种法律情景中的绩效差异.
研究的目的:
- 解决CCVG中传统样本平均指标的局限性.
- 引入新的评估指标,在不同类型的案件中提供公平,平衡的绩效评估.
- 提出一个新的框架,以提高CCVG模型在不同类型的案件中公平地提高性能.
主要方法:
- 提出了两个新的以案例类型为导向的评估指标:以案例类型为导向的文本生成 (CTG) 和以案例类型为导向的失衡性能 (CIP).
- 对于公平评估,CTG将每个案例类型赋予相同的权重.
- CIP通过将个案类型的表现与整体表现进行比较来衡量业绩不平衡,并得到三个阐明定理的支持.
主要成果:
- 拟议的CTG和CIP指标提供了对CCVG模型的公平和细微的评估,与传统的平均指标不同.
- CIP有效地识别和量化不同法律案例类型的业绩不平衡.
- 电荷引导编码解码器 (CGED) 框架在预训练的语言模型中,在各种案例类型中显示出改进和公平的性能.
结论:
- 新型以案件类型为导向的指标 (CTG和CIP) 为刑事法庭观点生成提供了更公平,更有洞察力的评估.
- 这些指标对于理解和减轻法律文本总结模型中的性能差异至关重要.
- CGED框架为提高CCVG系统的公平性和有效性提供了一种实际解决方案.
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