Affordable AI Assistants with Knowledge Graph of Thoughts
It addresses the problem of making AI assistants more affordable and effective for users, though it appears incremental as it builds on existing LLM and knowledge graph techniques.
The paper tackles the high costs and limited success rates of LLM-driven AI assistants by proposing Knowledge Graph of Thoughts (KGoT), which integrates LLM reasoning with dynamic knowledge graphs, achieving a 29% improvement in task success on the GAIA benchmark and reducing operational costs by over 36x compared to GPT-4o.
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 operational costs and limited success rates on complex benchmarks like GAIA. To address these issues, we propose Knowledge Graph of Thoughts (KGoT), an innovative AI assistant architecture that integrates LLM reasoning with dynamically constructed knowledge graphs (KGs). KGoT extracts and structures task-relevant knowledge into a dynamic KG representation, iteratively enhanced through external tools such as math solvers, web crawlers, and Python scripts. Such structured representation of task-relevant knowledge enables low-cost models to solve complex tasks effectively while also minimizing bias and noise. For example, KGoT achieves a 29% improvement in task success rates on the GAIA benchmark compared to Hugging Face Agents with GPT-4o mini. Moreover, harnessing a smaller model dramatically reduces operational costs by over 36x compared to GPT-4o. Improvements for other models (e.g., Qwen2.5-32B and Deepseek-R1-70B) and benchmarks (e.g., SimpleQA) are similar. KGoT offers a scalable, affordable, versatile, and high-performing solution for AI assistants.