FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning
<|reference_start|>FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning: Instruction tuning is an important step in making language models useful for direct user interaction. However, many legal tasks remain out of reach for most open LLMs and there do not yet exist any large scale instruction datasets for the domain. This critically limits research in this application area. In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples. We present evidence that domain-specific pretraining and instruction tuning improve performance on LegalBench, including improving Flan-T5 XL by 8 points or 16\% over the baseline. However, the effect does not generalize across all tasks, training regimes, model sizes, and other factors. LawInstruct is a resource for accelerating the development of models with stronger information processing and decision making capabilities in the legal domain.<|reference_end|>
@article{niklaus2024flawn-t5:,
    title={FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data
  Mixtures for Legal Reasoning},
    author={Joel Niklaus, Lucia Zheng, Arya D. McCarthy, Christopher Hahn, Brian
  M. Rosen, Peter Henderson, Daniel E. Ho, Garrett Honke, Percy Liang,
  Christopher Manning},
    journal={arXiv preprint arXiv:2404.02127},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02127},
    primaryClass={cs.CL cs.AI cs.LG}
}