ValueEval

Systematic Study of Schwartz Value Detection in Political Texts: Context, Scale, and Moral Knowledge

This paper investigates when additional context, larger models, or retrieved moral knowledge improve detection of Schwartz human values in political text using the ValueEval benchmark format. Key findings show that full-document context helps supervised DeBERTa encoders (+3.8–4.8 macro-F1) but not zero-shot LLMs, while RAG with a curated moral knowledge base consistently benefits all model families under early fusion. Scaling model size does not guarantee gains, and simple early fusion outperforms more complex RAG variants. The study recommends jointly evaluating context, knowledge, and model family rather than assuming larger inputs or models universally improve value-sensitive NLP.