Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibration
📰 ArXiv cs.AI
Schwartz higher-order values can improve sentence-level human value detection in a compute-frugal setting
Action Steps
- Utilize Schwartz higher-order categories to enhance human value detection in sentences
- Implement hierarchical gating and calibration techniques to improve model performance
- Compare the effectiveness of different architectures, such as direct supervised transformers and cascades, in detecting human values
- Evaluate the impact of compute-frugal budgets on model performance and accuracy
Who Needs to Know This
NLP researchers and AI engineers working on human value detection tasks can benefit from this study, as it provides insights into the effectiveness of Schwartz higher-order values in improving detection accuracy
Key Insight
💡 Schwartz higher-order values can improve the accuracy of human value detection in sentences, even in compute-frugal settings
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💡 Schwartz higher-order values can boost human value detection in sentences
Key Takeaways
Schwartz higher-order values can improve sentence-level human value detection in a compute-frugal setting
Full Article
Title: Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibration
Abstract:
arXiv:2602.00913v3 Announce Type: replace-cross Abstract: Human value detection from single sentences is a sparse, imbalanced multi-label task. We study whether Schwartz higher-order (HO) categories help this setting on ValueEval'24 / ValuesML (74K English sentences) under a compute-frugal budget. Rather than proposing a new architecture, we compare direct supervised transformers, hard HO$\rightarrow$values pipelines, Presence$\rightarrow$HO$\rightarrow$values cascades, compact instruction-tuned
Abstract:
arXiv:2602.00913v3 Announce Type: replace-cross Abstract: Human value detection from single sentences is a sparse, imbalanced multi-label task. We study whether Schwartz higher-order (HO) categories help this setting on ValueEval'24 / ValuesML (74K English sentences) under a compute-frugal budget. Rather than proposing a new architecture, we compare direct supervised transformers, hard HO$\rightarrow$values pipelines, Presence$\rightarrow$HO$\rightarrow$values cascades, compact instruction-tuned
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