{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Machine Learning Tech Brief By HackerNoon","title":"Your Severity Weights Are Made Up (And That's the Problem)","html":"","width":"100%","height":180,"duration":467,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/your-severity-weights-are-made-up-and-thats-the-problem.\nMost teams weight LLM hallucination types by gut feel or not at all. Here's a framework for deriving severity weights that actually hold up.\nCheck more stories related to machine-learning at: https://hackernoon.com/c/machine-learning.\n You can also check exclusive content about #machine-learning, #ai-hallucinations, #llm-hallucination, #ai-weights, #ai-equal-weighting, #hallucination-rate, #binary-hallucination, #hackernoon-top-story, and more.\nThis story was written by: @praveenmyakala. Learn more about this writer by checking @praveenmyakala's about page,\n and for more stories, please visit hackernoon.com.\nMost teams either treat every hallucination type as equally bad or assign severity by gut feel in a Slack thread. Both are made up. Real severity weights come from four things: downstream cost, reversibility, detectability, and frequency under load. \n \n ","thumbnail_url":"https://img.transistorcdn.com/KyA01h2FD2insgk-wX_xzV6vbJnTNl2BvPYVL-XaI9A/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMjcyLzE2ODM1/ODI0ODgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}