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Will DeepMind's GenRM improve LLM benchmark scores by a significant margin by end of Q1 2025?
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Google DeepMind•University of Toronto•MILA•UCLA•Generative Reward Modeling•DeepMind•Large Language Models
Resolution / Starting Odds
Yes • 50%
No • 50%
Published benchmark results in academic papers or tech blogs
Story
Google DeepMind's GenRM Enhances LLM Accuracy by Verifying Outputs
Sep 3, 2024, 01:04 PM
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.
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Similar markets
Yes • 50%
No • 50%
GLUE • 25%
SuperGLUE • 25%
SQuAD • 25%
Other • 25%
25% gain in MATH • 25%
15% gain in other domains • 25%
Integration into multiple commercial products • 25%
Other • 25%
Yes • 50%
No • 50%
Yes • 50%
No • 50%
Reduction in hallucinations • 25%
Improvement in factual accuracy • 25%
Enhanced numerical and statistical data integration • 25%
Other • 25%
Yes • 50%
No • 50%