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.

该研究提出了一种法律指令数据集（LawInstruct），通过领域特定的预训练和指令调整来改进法律任务处理性能，加快发展在法律领域中具备更强信息处理和决策能力的模型。

FLawN-T5: 法律推理的有效指导调整数据混合的实证研究

FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data  Mixtures for Legal Reasoning

We introduce FinTral, a suite of state-of-the-art multimodal large language
models (LLMs) built upon the Mistral-7b model and tailored for financial
analysis. FinTral integrates textual, numerical, tabular, and image data. We
enhance FinTral with domain-specific pretraining, instruction fine-tuning, and
RLAIF training by exploiting a large collection of textual and visual datasets
we curate for this work. We also introduce an extensive benchmark featuring
nine tasks and 25 datasets for evaluation, including hallucinations in the
financial domain. Our FinTral model trained with direct preference optimization
employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R,
demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5
in all tasks and surpasses GPT-4 in five out of nine tasks, marking a
significant advancement in AI-driven financial technology. We also demonstrate
that FinTral has the potential to excel in real-time analysis and
decision-making in diverse financial contexts.

介绍了一种名为 FinTral 的多模态大型语言模型套件，通过使用领域特定的预训练、指导微调和 RLAIF 训练等方法，结合文本、数值、表格和图像数据进行金融分析，并演示了其在金融技术中能够在实时分析和决策方面取得显著进展。