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

2026-04-23 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A new framework has been proposed for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on individual annotators' rationales. This work utilizes a dataset featuring disaggregated natural language inference (NLI) annotations and annotator-provided explanations. Predictions are conditioned on both annotator identity and demographic metadata via a representation-level User Passport mechanism. The framework introduces two explainer architectures: a post-hoc prompt-based explainer and a prefixed bridge explainer, which transfers annotator-conditioned classifier representations into a generative model. This design aims to generate explanations aligned with individual annotator perspectives. Incorporating explanation modeling significantly improves predictive performance compared to a baseline annotator-aware classifier, with the prefixed bridge approach demonstrating superior label alignment and semantic consistency, while the post-hoc method achieves stronger lexical similarity.

Key takeaway

For research scientists developing NLI models, integrating annotator-specific rationales into your predictive and generative components can substantially improve performance and provide a more faithful representation of disagreement. Consider implementing a prefixed bridge explainer for better label alignment and semantic consistency in generated explanations, especially when fine-grained perspective modeling is critical for your application.

Key insights

Modeling explanations as fine-grained perspectives enriches disagreement representation and improves predictive performance.

Principles

• Annotator rationales provide fine-grained perspective signals.
• Conditioning on annotator identity improves prediction.

Method

The framework jointly models annotator-specific label prediction and explanations, using a User Passport and two explainer architectures: post-hoc prompt-based and prefixed bridge.

In practice

• Use User Passports for demographic conditioning.
• Employ prefixed bridge explainers for semantic consistency.

Topics

• Annotator-Specific Rationales
• Perspectivist Modeling
• Explanation Generation
• Natural Language Inference
• User Passport

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.