Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification

2026-01-29

AI Summary

Existing evaluation methods for large language models (LLMs) in causal reasoning tasks rely heavily on string matching or superficial metrics, which often fail to accurately assess the semantic correctness of generated answers. To address this limitation, this work proposes DoVerifier—a novel symbolic verification approach that introduces a formal mechanism to validate whether an LLM-generated causal expression is derivable from a given causal graph using do-calculus and rules from probability theory. DoVerifier effectively identifies semantically correct responses that conventional metrics erroneously classify as incorrect. Evaluated on both synthetic datasets and established causal question-answering benchmarks, the method significantly enhances the rigor and informativeness of model evaluation, uncovering latent reasoning capabilities previously obscured by inadequate assessment protocols.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) are increasingly being applied to tasks that involve causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that do not capture whether the output of a model is formally valid under the semantics of causal reasoning. To address this, we propose DoVerifier, a simple symbolic verifier that checks whether LLM-generated causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory. This allows us to recover correct answers to causal queries that would otherwise be marked incorrect due to superficial differences in their causal semantics. Our evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces, offering a more rigorous and informative way to evaluate LLMs on causal reasoning.

Problem

• causal reasoning
• large language models
• semantic correctness
• benchmark evaluation
• do-calculus

Innovation

• symbolic verification
• causal reasoning
• do-calculus
• large language models
• semantic correctness