Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives
Introduction of SAGE (ScAffolded Generative models for Explanation), a neuro-symbolic framework combining cognitive model transparency with LLM generative flexibility for pragmatic reasoning. SAGE decomposes pragmatic processes into three distinct modules: proposers (LM-based alternative generation), evaluators (assessment of semantics/complexity), and selectors (rule-based decision logic). Empirical validation across three case studies—referential expression generation, M-implicatures, and Gric
Analysis
TL;DR
- Introduction of SAGE (ScAffolded Generative models for Explanation), a neuro-symbolic framework combining cognitive model transparency with LLM generative flexibility for pragmatic reasoning.
- SAGE decomposes pragmatic processes into three distinct modules: proposers (LM-based alternative generation), evaluators (assessment of semantics/complexity), and selectors (rule-based decision logic).
- Empirical validation across three case studies—referential expression generation, M-implicatures, and Gricean conversational implicatures—demonstrates high accuracy and superior performance over traditional baselines.
- Component-level analysis reveals an asymmetry where LM proposers effectively generate suitable alternatives, while LM evaluators excel at intuitive judgments rather than formal theoretical metrics.
Why It Matters
This research addresses a critical bottleneck in computational pragmatics: the manual specification of alternative sets required by formal models. By leveraging LLMs to automatically generate these alternatives within a structured, interpretable framework, SAGE offers a scalable path toward more human-like pragmatic reasoning systems. For AI practitioners, it highlights the potential and current limitations of using LLMs as cognitive components in hybrid neuro-symbolic architectures, particularly regarding the distinction between intuitive and formal evaluation capabilities.
Technical Details
- Framework Architecture: SAGE integrates Large Language Models with symbolic rule-based systems. It utilizes LLMs as "proposers" to create open-ended spaces of candidate expressions or interpretations, while "selectors" apply cognitively motivated, rule-based steps to make final decisions.
- Module Breakdown: The system consists of proposers (generating alternatives), evaluators (assessing attributes like semantics, complexity, or typicality), and selectors (implementing the core pragmatic reasoning rules).
- Evaluation Methodology: The framework was tested using standard computational cognitive modeling techniques, including ablation studies, baseline comparisons, and quantitative fitting to human behavioral data.
- Case Studies: The model was applied to three specific pragmatic tasks: generating referential expressions, deriving manner (M-) implicatures, and computing Gricean conversational implicatures.
- Key Finding on Evaluators: While proposers performed robustly, evaluators showed a limitation in applying formal theoretical measures, performing better when providing intuitive judgments compared to strict semantic or complexity assessments.
Industry Insight
- Hybrid Model Design: Developers should consider modularizing LLM usage in complex reasoning tasks, separating generation (proposal) from evaluation and selection to maintain interpretability and control.
- Limitation Awareness: When using LLMs for pragmatic evaluation, rely on their intuitive linguistic judgment capabilities rather than expecting them to strictly adhere to formal semantic or complexity metrics without additional scaffolding.
- Explainability Focus: The success of SAGE underscores the growing industry demand for neuro-symbolic approaches that offer both the performance of deep learning and the explanatory power required for scientific and regulatory scrutiny in AI systems.
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