Show HN: Catelingo – Constraint-Based Semantic Validity Checker for LLM Outputs (github.com)

Catelingo has been introduced as a new constraint-based semantic validity checker aimed at improving the outputs of large language models (LLMs). This innovative tool specifically targets semantic errors—such as temporal impossibilities and numerical violations—that can occur in otherwise fluent and high-likelihood generated text. Unlike traditional methods that rely on reasoning or external knowledge bases, Catelingo operates post-hoc by treating semantic validity as a constraint satisfaction problem. It uses explicit semantic constraints that are propagated through syntactic dependencies to determine if a statement is satisfactory (SAT), unsatisfactory (UNSAT), or unknown. This approach allows it to catch semantic failures without complex inference processes. The significance of Catelingo lies in its model-agnostic nature and lightweight implementation, making it a versatile tool for verifying LLM outputs across various domains, from finance to poetry. Developers can easily integrate it into their workflows to enhance the reliability of generated text without retraining models or relying on external data. As the AI/ML community continues to grapple with issues of output validity, tools like Catelingo could pave the way for more trustworthy language generation, ensuring that produced texts meet stringent semantic constraints. The repository is available for those interested in testing its capabilities, reinforcing the concept that semantic validity verification can be achieved independently of other generative processes.