How does the cross-domain generalization performance of DeepSeek-R1 and o1-preview models vary when evaluated

Abstract

Abstract: In an era dominated by Large Language Models (LLMs), understanding their capabilities and limitations, especially in high-stakes fields like law, is crucial. While LLMs such as Meta's LLaMA, OpenAI's ChatGPT, Google's Gemini, DeepSeek, and other emerging models are increasingly integrated into legal workflows, their performance in multilingual, jurisdictionally diverse, and adversarial contexts remains insufficiently explored. This work evaluates LLaMA and Gemini on multilingual legal and non-legal benchmarks, and assesses their adversarial robustness in legal tasks through character and word-

Research Question

How does the cross-domain generalization performance of DeepSeek-R1 and o1-preview models vary when evaluated on multilingual legal benchmarks with different time-compute budgets

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

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Provenance