How does the accuracy of DeepSeek-R1 and o1-preview scale with chain-of-thought length (number of reasoning to

Abstract

Abstract: Despite increasing discussions on open-source Artificial Intelligence (AI), existing research lacks a discussion on the transparency and accessibility of state-of-the-art (SoTA) Large Language Models (LLMs). The Open Source Initiative (OSI) has recently released its first formal definition of open-source software. This definition, when combined with standard dictionary definitions and the sparse published literature, provide an initial framework to support broader accessibility to AI models such as LLMs, but more work is essential to capture the unique dynamics of openness in AI. In addition,

Research Question

How does the accuracy of DeepSeek-R1 and o1-preview scale with chain-of-thought length (number of reasoning tokens) across Chinese vs. English legal benchmarks, and what is the token-efficiency trade-off (accuracy per inference cost)?

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Correction Record

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Provenance