Modification of the Test Protocols Generation with the Knowledge Graphs

Graph4LLM: A Systematic Survey of Graph-Enhanced Large Language Models

This survey examines how these methods integrate graphs into various stages of the LLM pipeline, including the input, model, and output phases, and outlines the challenges and future research directions for developing more efficient and interpretable solutions.

PAMI-GPT: knowledge-grounded LLMs for reliable pattern mining workflow generation

Experimental evaluation on 300 realistic pattern mining tasks demonstrates consistent improvements in algorithm configuration accuracy, parameter compliance, and dataset specification correctness across zero-shot, one-shot, and few-shot settings, highlighting the effectiveness of inference-time domain grounding for ena...

A Knowledge Graph–Augmented Large Language Model for Automated Quality Compliance Checking in Construction

This research proposes a large language model–based approach combined with structured algorithms that process domain-specific competency questions and unstructured construction documents to build ontologies and extract structured knowledge graphs to support the automated generation of quality inspection checklists and...

From Use Cases to Sequence Diagrams: Schema-Constrained Generation with Large Language Models

Sequence diagrams are widely used to model interactions among system components and external actors by explicitly describing how operations are executed over time. However, constructing sequence diagrams manually is tedious and time-consuming in practice, and their automated generation remains largely unresolved. In th...

From Unstructured Procedural Text to Knowledge Graphs: Comparing LLM-Based Extraction Strategies

This paper addresses the automated construction of knowledge graphs from natural language procedural descriptions, developing a generic approach for transforming unstructured expert knowledge into structured knowledge graphs that support downstream retrieval and question-answering applications.

HULAT-UC3M at TSAR 2025 Shared Task: A Prompt-Based Approach using Lightweight Language Models for Readability-Controlled Text Simplification