# Designing NLP Systems That Adapt to Diverse Worldviews

arXiv: https://arxiv.org/abs/2405.11197 · HTML: https://mlsanity.com/paper/2405.11197/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 14 of 14)

- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Social Bias Frames: Reasoning about Social and Power Implications of Language](https://mlsanity.com/paper/1911.03891/) (2019)
- [Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection](https://mlsanity.com/paper/2111.07997/) (2021)
- [Human uncertainty makes classification more robust](https://mlsanity.com/paper/1908.07086/) (2019)
- [Adversarial Filters of Dataset Biases](https://mlsanity.com/paper/2002.04108/) (2020)
- [What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://mlsanity.com/paper/2010.03532/) (2020)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [The Ecological Fallacy in Annotation: Modelling Human Label Variation goes beyond Sociodemographics](https://mlsanity.com/paper/2306.11559/) (2023)
- [Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection](https://mlsanity.com/paper/2106.15896/) (2021)
- [Towards Robustifying NLI Models Against Lexical Dataset Biases](https://mlsanity.com/paper/2005.04732/) (2020)
- [Did they answer? Subjective acts and intents in conversational discourse](https://mlsanity.com/paper/2104.04470/) (2021)
- [How Crowd Worker Factors Influence Subjective Annotations: A Study of Tagging Misogynistic Hate Speech in Tweets](https://mlsanity.com/paper/2309.01288/) (2023)
- [Subjective textitIsms? On the Danger of Conflating Hate and Offence in Abusive Language Detection](https://mlsanity.com/paper/2403.02268/) (2024)

## Built on by (most cited first, 2 of 2)

- [Taking a SEAT: Predicting Value Interpretations from Sentiment, Emotion, Argument, and Topic Annotations](https://mlsanity.com/paper/2510.01976/) (2025)
- [Who Warmed the Archives? LLMs Overestimate Historical Warmth](https://mlsanity.com/paper/2609.37499/) (2026)

## Abstract (arXiv)

Natural Language Inference (NLI) is foundational for evaluating language understanding in AI. However, progress has plateaued, with models failing on ambiguous examples and exhibiting poor generalization. We argue that this stems from disregarding the subjective nature of meaning, which is intrinsically tied to an individual's textitweltanschauung (which roughly translates to worldview). Existing NLP datasets often obscure this by aggregating labels or filtering out disagreement. We propose a perspectivist approach: building datasets that capture annotator demographics, values, and justifications for their labels. Such datasets would explicitly model diverse worldviews. Our initial experiments with a subset of the SBIC dataset demonstrate that even limited annotator metadata can improve model performance.