From Large Language Model Predicates to Logic Tensor Networks: Neurosymbolic Offer Validation in Regulated Procurement

2026-04-07

AI Summary

This study addresses the challenge in regulated public procurement where bid validation must simultaneously ensure factual accuracy and legal auditability—a balance difficult to achieve with conventional approaches that struggle to integrate semantic understanding with rule-based interpretability. To bridge this gap, the authors propose a neuro-symbolic method that synergistically combines large language models (LLMs) with Logic Tensor Networks (LTNs). Specifically, LLMs are employed to extract semantic predicates from textual bid documents, which are then integrated into an LTN framework that encodes domain-specific regulatory rules to perform auditable, logic-driven inference. Experimental evaluation on real-world procurement documents demonstrates that the proposed approach not only maintains competitive performance but also significantly enhances decision interpretability and system modularity, thereby offering robust support for explainable artificial intelligence (XAI) in high-stakes regulatory contexts.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Neuro-Symbolic Learning
• Humans and AI: Explainable AI (XAI) for Human Understanding

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

We present a neurosymbolic approach, i.e., combining symbolic and subsymbolic artificial intelligence, to validating offer documents in regulated public institutions. We employ a language model to extract information and then aggregate with an LTN (Logic Tensor Network) to make an auditable decision. In regulated public institutions, decisions must be made in a manner that is both factually correct and legally verifiable. Our neurosymbolic approach allows existing domain-specific knowledge to be linked to the semantic text understanding of language models. The decisions resulting from our pipeline can be justified by predicate values, rule truth values, and corresponding text passages, which enables rule checking based on a real corpus of offer documents. Our experiments on a real corpus show that the proposed pipeline achieves performance comparable to existing models, while its key advantage lies in its interpretability, modular predicate extraction, and explicit support for XAI (Explainable AI).

Problem

• offer validation
• regulated procurement
• neurosymbolic AI
• explainable AI
• Logic Tensor Networks

Innovation

• neurosymbolic
• Logic Tensor Networks
• Explainable AI
• predicate extraction
• regulated procurement