Robert Gerstenberger
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View article: Affordable AI Assistants with Knowledge Graph of Thoughts
Affordable AI Assistants with Knowledge Graph of Thoughts Open
Large Language Models (LLMs) are revolutionizing the development of AI assistants capable of performing diverse tasks across domains. However, current state-of-the-art LLM-driven agents face significant challenges, including high operation…
View article: Reasoning Language Models: A Blueprint
Reasoning Language Models: A Blueprint Open
Reasoning language models (RLMs), also known as Large Reasoning Models (LRMs), such as OpenAI's o1 and o3, DeepSeek-R1, and Alibaba's QwQ, have redefined AI's problem-solving capabilities by extending LLMs with advanced reasoning mechanism…
View article: Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments
Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments Open
Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains such as data mining and search engines. Simultaneously, the…
View article: CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks
CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks Open
Large Language Models (LLMs) are transforming a wide range of domains, yet verifying their outputs remains a significant challenge, especially for complex open-ended tasks such as consolidation, summarization, and knowledge extraction. To …
View article: Graph of Thoughts: Solving Elaborate Problems with Large Language Models
Graph of Thoughts: Solving Elaborate Problems with Large Language Models Open
We introduce Graph of Thoughts (GoT): a framework that advances prompting capabilities in large language models (LLMs) beyond those offered by paradigms such as Chain-of-Thought or Tree of Thoughts (ToT). The key idea and primary advantage…
View article: Demystifying Chains, Trees, and Graphs of Thoughts
Demystifying Chains, Trees, and Graphs of Thoughts Open
The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance through innovative prompting techniques. Among these, prompt engi…
View article: The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores
The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores Open
Graph databases (GDBs) are crucial in academic and industry applications. The key challenges in developing GDBs are achieving high performance, scalability, programmability, and portability. To tackle these challenges, we harness establish…
View article: Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries Open
Graph processing has become an important part of multiple areas of computer science, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Numerous graphs such as web or social ne…