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The ``arms race'' of Large Language Models (LLMs) demands new benchmarks to examine their progresses.
On random graphs I
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The dblp computer science bibliography: Evolution, research issues, perspectives
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Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
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Reinforcement learning for combinatorial optimization: A survey
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Petar Veličković and Charles Blundell · 2021
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Chatbot arena: An open platform for evaluating llms by human preference
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The clrs-text algorithmic reasoning language benchmark
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Learning to reason with llms
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https://openflights.org/
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Let your graph do the talking: Encoding structured data for llms
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
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