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Researchers from Google DeepMind, University of Toronto, MILA, and UCLA have introduced a novel approach called Generative Reward Modeling (GenRM). DeepMind's GenRM improves the accuracy of Large Language Models (LLMs) by training them to verify their own outputs using next-token prediction and chain-of-thought (CoT) reasoning. The approach leverages the text generation capabilities of LLMs to improve their performance.
DeepMind’s GenRM improves LLM accuracy by having models verify their own outputs: DeepMind's GenRM trains LLMs to verify responses based on next-token prediction and chain-of-thought (CoT) reasoning. https://t.co/XYlkJLRAq2 #AI #AIresearch
DeepMind's GenRM improves LLM accuracy by having models verify their own outputs https://t.co/hPa06tZBzW
"GraphRAG will subsume vector-only RAG and emerge as the default RAG architecture for most use cases." @prathle explores the powerful impact of #GraphRAG—the combination of knowledge graphs + RAG - in "The GraphRAG Manifesto.” Worth reading in case you missed it! 👇 👇… https://t.co/t9444mtkYr
