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Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability https://t.co/aeDZ7DUJ81 https://t.co/H3UuiDcuvw
[CL] Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability J Hron, L Culp, G Elsayed, R Liu… [Google DeepMind] (2024) https://t.co/wDOqB24TG6 - Study examines how hallucinations in LMs depend on scale and how detectable they are.… https://t.co/0B3FS6NYR7
Training Language Models on the Knowledge Graph: Insights on Hallucinations and Their Detectability abs: https://t.co/BicDJ1N2ZL New paper from Google DeepMind that trains LLMs of different scales on knowledge graphs as a controlled environment to study hallucinations and… https://t.co/UOcsyl5pGd
Recent studies highlight the challenges posed by hallucinations in large language models (LLMs), which can affect the quality and reliability of their outputs. A new paper from Google DeepMind explores training LLMs on knowledge graphs to better understand and detect these hallucinations. The research examines how the scale of language models influences the occurrence and detectability of hallucinations. Additionally, efforts are being made to improve the accuracy and consistency of LLMs when analyzing unstructured clinical notes in electronic medical records. Techniques such as groundedness detection and Azure AI Content Safety are being used to enhance the trustworthiness of AI models.
