From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

2026-07-21 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A neurosymbolic framework is proposed to lift Large Language Model (LLM) outputs into typed compositional derivations using Combinatory Categorial Grammar (CCG). This framework posits that the prefix-driven, type-completing dynamics of autoregressive LLM generation align closely with CCG's incremental processing model. It does not claim LLMs internally implement CCG, but rather that their outputs can be reconstructed in a principled, incremental, and auditable CCG manner. This approach has two key consequences: first, it extends beyond natural language to formal languages like Solidity, OWL, and SQL through the Curry-Howard correspondence, maintaining a fixed architecture with varying type systems. Second, it supports a dual-layer checking mechanism, including a compositional layer for structural failures and a content layer that verifies lifted structures against external knowledge sources, enabling early detection of hallucinated content.

Key takeaway

For NLP Engineers developing LLM applications, this framework offers a robust method to enhance output reliability and auditability. You should consider integrating CCG-based lifting to formally verify generated code or structured data, and to implement early hallucination detection. This approach improves trust in your LLM outputs by providing structural and content validation, particularly for critical applications involving formal languages or sensitive information.

Key insights

LLM outputs can be formally lifted into auditable compositional structures using CCG for enhanced verification.

Principles

• LLM autoregressive generation aligns with CCG's incremental processing.
• CCG reconstruction enables auditable, typed compositional derivations.
• Formal languages benefit from CCG lifting via Curry-Howard correspondence.

Method

The framework lifts LLM outputs into typed compositional derivations using CCG, then applies dual-layer checking for structural integrity and content accuracy against external knowledge.

In practice

• Reconstruct LLM outputs for formal language generation (e.g., Solidity, SQL).
• Implement dual-layer checking to detect LLM structural errors.
• Flag hallucinated content early using external knowledge sources.

Topics

• Large Language Models
• Combinatory Categorial Grammar
• Neurosymbolic AI
• Formal Languages
• Hallucination Detection
• Code Generation

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.