Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales

2026-04-23

AI Summary

This study addresses the challenge of modeling fine-grained perspective differences among individual annotators in natural language inference tasks, along with their underlying rationales. To this end, the authors propose a joint modeling framework that integrates annotator identity and demographic information through a novel “User Passport” mechanism. They further introduce two distinct interpreter architectures—posterior prompting and prefix bridging—to deeply incorporate annotator-specific reasoning into both prediction and explanation generation. Experimental results demonstrate that the proposed approach significantly outperforms baseline models that rely solely on annotator identity. Specifically, the prefix bridging architecture excels in label alignment and semantic consistency, while the posterior prompting variant achieves higher lexical similarity. Collectively, these advances enhance both the faithfulness and expressiveness of perspective-aware modeling.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Preferences

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators' provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, we condition predictions on both annotator identity and demographic metadata through a representation-level User Passport mechanism. We further introduce two explainer architectures: a post-hoc prompt-based explainer and a prefixed bridge explainer that transfers annotator-conditioned classifier representations directly into a generative model. This design enables explanation generation aligned with individual annotator perspectives. Our results show that incorporating explanation modeling substantially improves predictive performance over a baseline annotator-aware classifier, with the prefixed bridge approach achieving more stable label alignment and higher semantic consistency, while the post-hoc approach yields stronger lexical similarity. These findings indicate that modeling explanations as expressions of fine-grained perspective provides a richer and more faithful representation of disagreement. The proposed approaches advance perspectivist modeling by integrating annotator-specific rationales into both predictive and generative components.

Problem

• fine-grained perspectives
• annotator-specific rationales
• natural language inference
• disaggregated annotations
• explanation modeling

Innovation

• annotator-specific rationales
• User Passport
• prefixed bridge explainer
• perspectivist modeling
• fine-grained perspectives