Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? When Hard Gating Hurts

• URL: http://arxiv.org/abs/2602.00913v1
• Date: Sat, 31 Jan 2026 21:50:35 GMT
• Title: Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? When Hard Gating Hurts
• Abstract summary: We study whether Schwartz higher-order (HO) categories provide usable structure under compute-frugal budget (single 8 GB GPU)<n>We compare direct supervised transformers, (ii) HO$rightarrow$values pipelines that enforce the hierarchy with hard masks, and (iii) Presence$rightarrow$HO$rightarrow$values cascades, alongside low-cost add-ons (lexica, short context, topics)<n>Overall, HO structure is useful descriptively, but enforcing it with hard gates hurts sentence-level value detection; robust improvements come from calibration and lightweight ensembling.
• Score: 3.0339361014383397
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sentence-level human value detection is typically framed as multi-label classification over Schwartz values, but it remains unclear whether Schwartz higher-order (HO) categories provide usable structure. We study this under a strict compute-frugal budget (single 8 GB GPU) on ValueEval'24 / ValuesML (74K English sentences). We compare (i) direct supervised transformers, (ii) HO$\rightarrow$values pipelines that enforce the hierarchy with hard masks, and (iii) Presence$\rightarrow$HO$\rightarrow$values cascades, alongside low-cost add-ons (lexica, short context, topics), label-wise threshold tuning, small instruction-tuned LLM baselines ($\le$10B), QLoRA, and simple ensembles. HO categories are learnable from single sentences (e.g., the easiest bipolar pair reaches Macro-$F_1\approx0.58$), but hard hierarchical gating is not a reliable win: it often reduces end-task Macro-$F_1$ via error compounding and recall suppression. In contrast, label-wise threshold tuning is a high-leverage knob (up to $+0.05$ Macro-$F_1$), and small transformer ensembles provide the most consistent additional gains (up to $+0.02$ Macro-$F_1$). Small LLMs lag behind supervised encoders as stand-alone systems, yet can contribute complementary errors in cross-family ensembles. Overall, HO structure is useful descriptively, but enforcing it with hard gates hurts sentence-level value detection; robust improvements come from calibration and lightweight ensembling.

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