Image de couverture de livre

How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Heim, Desiree, Meyer, Lars-Peter, Schröder, Markus, Frey, Johannes, Dengel, Andreas
Format:	Preprint
Publié:	2025
Sujets:	Artificial Intelligence Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Using Large Language Models to Generate Authentic Multi-agent Knowledge Work Datasets
par: Heim, Desiree, et autres
Publié: (2024)

Assessing SPARQL capabilities of Large Language Models
par: Meyer, Lars-Peter, et autres
Publié: (2024)

LLM-KG-Bench 3.0: A Compass for SemanticTechnology Capabilities in the Ocean of LLMs
par: Meyer, Lars-Peter, et autres
Publié: (2025)

Scaling Laws for Downstream Task Performance of Large Language Models
par: Isik, Berivan, et autres
Publié: (2024)

CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction
par: Foroutan, Neda, et autres
Publié: (2024)

LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
par: Meyer, Lars-Peter, et autres
Publié: (2023)

Data Collection of Real-Life Knowledge Work in Context: The RLKWiC Dataset
par: Bakhshizadeh, Mahta, et autres
Publié: (2024)

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
par: Zhou, Chenxi, et autres
Publié: (2025)

Context‐centric proactive information delivery for Knowledge Work support: Opportunities, challenges, and directions. An Annual Review of Information Science and Technology (ARIST)
par: Mahta Bakhshizadeh, et autres
Publié: (2026)

Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance
par: Brei, Felix, et autres
Publié: (2024)

Characterizing Knowledge Graph Tasks in LLM Benchmarks Using Cognitive Complexity Frameworks
par: Todorovikj, Sara, et autres
Publié: (2025)

ARUQULA -- An LLM based Text2SPARQL Approach using ReAct and Knowledge Graph Exploration Utilities
par: Brei, Felix, et autres
Publié: (2025)

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future
par: Saleem, Summra, et autres
Publié: (2025)

How to inject knowledge efficiently? Knowledge Infusion Scaling Law for Pre-training Large Language Models
par: Lv, Kangtao, et autres
Publié: (2025)

Knowledge Prompting: How Knowledge Engineers Use Large Language Models
par: Koutsiana, Elisavet, et autres
Publié: (2024)

Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
par: Reitemeyer, Benedikt, et autres
Publié: (2025)

Modular Graph Extraction for Handwritten Circuit Diagram Images
par: Bayer, Johannes, et autres
Publié: (2024)

Accelerating Knowledge Graph and Ontology Engineering with Large Language Models
par: Shimizu, Cogan, et autres
Publié: (2024)

Scaling Law for Language Models Training Considering Batch Size
par: Shuai, Xian, et autres
Publié: (2024)

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations
par: Saleem, Summra, et autres
Publié: (2024)

Knowledge Graphs, Large Language Models, and Hallucinations: An NLP Perspective
par: Lavrinovics, Ernests, et autres
Publié: (2024)

Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?
par: Chen, Xuanyu, et autres
Publié: (2025)

Performance Law of Large Language Models
par: Wu, Chuhan, et autres
Publié: (2024)

Model Merging Scaling Laws in Large Language Models
par: Wang, Yuanyi, et autres
Publié: (2025)

GenAIReading: Augmenting Human Cognition with Interactive Digital Textbooks Using Large Language Models and Image Generation Models
par: Morita, Ryugo, et autres
Publié: (2025)

Temporal Scaling Law for Large Language Models
par: Xiong, Yizhe, et autres
Publié: (2024)

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
par: Liu, Xiaoze, et autres
Publié: (2024)

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving
par: Srinivas, Sakhinana Sagar, et autres
Publié: (2024)

Observational Scaling Laws and the Predictability of Language Model Performance
par: Ruan, Yangjun, et autres
Publié: (2024)

Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline
par: Vogelsang, Andreas, et autres
Publié: (2024)

Making Large Language Models Perform Better in Knowledge Graph Completion
par: Zhang, Yichi, et autres
Publié: (2023)

Leverage Knowledge Graph and Large Language Model for Law Article Recommendation: A Case Study of Chinese Criminal Law
par: Chen, Yongming, et autres
Publié: (2024)

Scaling Laws for Fact Memorization of Large Language Models
par: Lu, Xingyu, et autres
Publié: (2024)

Scaling Laws for Discriminative Classification in Large Language Models
par: Dean Wyatte, et autres
Publié: (2025)

Scaling Laws for Discriminative Classification in Large Language Models
par: Wyatte, Dean, et autres
Publié: (2024)

Scaling Laws for State Dynamics in Large Language Models
par: Li, Jacob X, et autres
Publié: (2025)

Evaluating the Impact of Compression Techniques on Task-Specific Performance of Large Language Models
par: Khanal, Bishwash, et autres
Publié: (2024)

Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks
par: Tao, Hongyuan, et autres
Publié: (2025)

How Well Do Large-Scale Chemical Language Models Transfer to Downstream Tasks?
par: Sagawa, Tatsuya, et autres
Publié: (2026)

An Analysis of Temporal Dropout in Earth Observation Time Series for Regression Tasks
par: Miranda, Miro, et autres
Publié: (2025)