R²-CoD: Understanding Text-Graph Complementarity in Relational Reasoning via Knowledge Co-Distillation

ACL ARR 2025 July Submission1104 Authors

Abstract: Relational reasoning lies at the core of many NLP tasks, drawing on complementary signals from text and graphs. While prior research has investigated how to leverage this dual complementarity, a detailed and systematic understanding of text-graph interplay and its effect on hybrid models remains underexplored. We take an analysis-driven approach to investigate text–graph representation complementarity via a unified architecture that supports knowledge co-distillation (CoD). We explore five tasks involving relational reasoning that differ in how text and graph structures encode the information needed to solve that task. By tracking how these dual representations evolve during training, we uncover interpretable patterns of alignment and divergence, and provide insights into when and why their integration is beneficial.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: text-graph complementarity, relational reasoning, knowledge co-distillation, representation analysis

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 1104