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Will DeepMind's GenRM be cited in at least 50 academic papers by end of 2025?
2
Google DeepMind•University of Toronto•MILA•UCLA•Generative Reward Modeling•DeepMind•Large Language Models
Resolution / Starting Odds
Yes • 50%
No • 50%
Academic databases such as Google Scholar or PubMed
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%
Less than 50 • 33%
50-100 • 33%
More than 100 • 34%
G. Jain's paper • 25%
N. Hegde's paper • 25%
A. Kusupati's paper • 25%
A. Nagrani's paper • 25%
Yes • 50%
No • 50%
Yes • 50%
No • 50%
0-5 collaborations • 25%
6-10 collaborations • 25%
11-15 collaborations • 25%
More than 15 collaborations • 25%