Tool Grounding

Tool grounding in AI focuses on ensuring that large language models (LLMs) and other AI systems accurately connect their outputs to relevant external information sources, such as knowledge graphs, videos, or text corpora. Current research emphasizes improving the grounding process in various applications, including question answering, video understanding, and knowledge graph reasoning, often employing retrieval-augmented generation (RAG) and techniques like semantic pruning in knowledge graphs or time-aware training objectives for video-text models. Successful tool grounding is crucial for enhancing the reliability and accuracy of AI systems, mitigating issues like hallucinations and improving their ability to perform complex tasks requiring interaction with the real world or external knowledge bases.