Comparative Analysis of Domain-Adaptive Pre-training and Instruction Fine-tuning for Zero-Shot Cross-Lingual Legal Retrieval

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 performance of domain-adaptive pre-training on LEMUR compare to instruction fine-tuning in zero-shot cross-lingual legal retrieval tasks when evaluated on adversarial perturbation benchmarks like MMLU or BIG-Bench?

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