SAGE: A Neuro-Symbolic Framework for Pragmatic Reasoning with Flexible Alternative Generation
Researchers introduce SAGE (ScAffolded Generative models for Explanation), a neuro-symbolic framework that integrates language models with cognitive models to address pragmatic reasoning. SAGE decomposes pragmatic tasks into proposers (generating alternatives), evaluators (assessing alternatives), and selectors (implementing rule-based decisions), and is tested on referential expression generation, M-implicatures, and Gricean implicatures. The framework achieved high accuracy and often outperformed baselines, but analysis revealed that language model proposers excel at generating alternatives, while evaluators are better at intuitive rather than formal judgments.
Why it matters: This work represents a notable advance in computational pragmatics by providing a flexible, interpretable approach that leverages both cognitive modeling and generative language models to improve context-sensitive language understanding and production in AI.
Full story at: arXiv Computation and Language ↗