Akirato/LLM-KG-Reasoning

LLM-KG-Reasoning is a legacy research project (over 3 years old) that explores the intersection of LLMs and structured knowledge. With only 112 stars and zero recent velocity, it serves more as a historical reference than a viable modern tool. The core problem it solves—measuring how well LLMs handle structured data—has been largely superseded by modern benchmarks (e.g., MMLU, Big-Bench) and more sophisticated 'GraphRAG' evaluation frameworks from major players like Microsoft. The defensibility is extremely low because the techniques used in 2021 (likely probing GPT-3 style completion models) do not translate well to the current era of instruction-tuned and RLHF-optimized models without significant updates. Frontier labs and evaluation platforms like LangSmith or Weights & Biases are already integrating automated factual checking against KGs as a standard feature, making this standalone implementation redundant.