用大模型自动量化身份融合程度，提升暴力风险评估效果超240%。
Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text
- 结合认知语言学与大模型，通过隐喻识别自动评估身份融合。
- 在暴力风险评估中性能提升超过240%，优于现有方法。
- 适合研究群体行为、心理建模与社会安全的学者使用。
量化身份融合——即个体心理上与另一实体或抽象目标（如宗教团体、政党、意识形态、价值观、品牌、信念等）融合的程度——对理解广泛的群体行为至关重要。我们提出认知语言身份融合评分（CLIFS），一种融合认知语言学与大语言模型的新指标，基于隐喻检测实现。与传统依赖受控调查或直接接触的图像和言语量表不同，CLIFS实现完全自动化、可扩展评估，且与现有口头量表高度一致。在基准测试中，CLIFS优于现有自动化方法及人工标注。作为概念验证，我们将CLIFS应用于暴力风险评估，证明其可使评估性能提升超过240%。基于我们识别出的新NLP任务与初步成功，强调需构建更大、更多样化的数据集，涵盖更多融合目标领域与文化背景，以增强泛化能力并推动该新兴领域发展。CLIFS模型与代码已公开于https://github.com/DevinW-sudo/CLIFS。
原文摘要 · Abstract (English)
Quantifying identity fusion -- the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.) -- is vital for understanding a wide range of group-based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score (CLIFS), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at https://github.com/DevinW-sudo/CLIFS.
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