Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. However, such internalized knowledge might be insufficient and incorrect, which could lead LLMs to generate factually wrong answers. Furthermore, fine-tuning LLMs to update their knowledge is expensive. To this end, we propose to augment the knowledge directly in the input of LLMs. Specifically, we first retrieve the relevant facts to the input question from the knowledge graph based on semantic similarities between the question and its associated facts. After that, we prepend the retrieved facts to the input question in the form of the prompt, which is then forwarded to LLMs to generate the answer. Our framework, Knowledge-Augmented language model PromptING (KAPING), requires no model training, thus completely zero-shot. We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.

COLUMBUS, OHIO – Ohio crews cleaning up a massive former Cold War-era uranium enrichment plant in Ohio plan this summer to deploy a high-tech helper: an autonomous, radiation-measuring robot that will roll through kilometers of large overhead pipes to spot potentially hazardous residual uranium. Officials say it's safer, more accurate and tremendously faster than having workers take external measurements to identify which pipes need to be removed and decontaminated at the Portsmouth Gaseous Diffusion Plant in Piketon. They say it could save taxpayers tens of millions of dollars on cleanups of that site and one near Paducah, Kentucky, which for decades enriched uranium for nuclear reactors and weapons. The RadPiper robot was developed at Carnegie Mellon University in Pittsburgh for the U.S. Department of Energy, which envisions using similar technology at other nuclear complexes such as the Savannah River Site in Aiken, South Carolina, and the Hanford Site in Richland, Washington. Roboticist William "Red" Whittaker, who began his career developing robots to help clean up the Three Mile Island nuclear power accident and now directs Carnegie Mellon's Field Robotics Center, said technology like RadPiper could transform key tasks in cleaning up the country's nuclear legacy.

Officials say using the RadPiper robot is safer, tremendously faster and more accurate than the current method of workers taking external measurements. They also say it could save tens of millions of public dollars on cleanups of that site and one near Paducah, Kentucky.

A pair of autonomous robots developed by Carnegie Mellon University's Robotics Institute will soon be driving through miles of pipes at the U.S. Department of Energy's former uranium enrichment plant in Piketon, Ohio, to identify uranium deposits on pipe walls. The CMU robot has demonstrated it can measure radiation levels more accurately from inside the pipe than is possible with external techniques. In addition to savings in labor costs, its use significantly reduces hazards to workers who otherwise must perform external measurements by hand, garbed in protective gear and using lifts or scaffolding to reach elevated pipes. DOE officials estimate the robots could save tens of millions of dollars in completing the characterization of uranium deposits at the Portsmouth Gaseous Diffusion Plant in Piketon, and save perhaps $50 million at a similar uranium enrichment plant in Paducah, Kentucky. "This will transform the way measurements of uranium deposits are made from now on," predicted William "Red" Whittaker, robotics professor and director of the Field Robotics Center.

I don't know if this has ever happened to you, but trust me, if you rely on your phone to help you navigate, it just might someday. My wife and I were en route from Raleigh to St. Louis, having enjoyed crossing the Smokies and the Cumberlands and just coming up on Paducah, Kentucky. Having been routed around a massive traffic jam in Knoxville by my phone, I was much pleased with it, and so listened carefully when it described a similar problem ahead in Paducah. The result: Following my phone's directions, I pulled off the interstate and began taking a series of roads through Paducah, just as I had done in Knoxville. My phone kept sending me down smaller and smaller roads, and soon we were out in the country, presumably bypassing the massive interstate jam ahead.