论文标题
调音还是不调音？法律案件的零拍模型
To Tune or Not To Tune? Zero-shot Models for Legal Case Entailment
论文作者
论文摘要
有越来越多的证据表明，对大型和多样化的监督数据集进行了微调的验证语言模型可以很好地转移到各种偏见的任务中。在这项工作中，我们调查了转移到法律领域的能力。为此，我们参加了Coliee 2021的法律案件构成任务，在该任务中，我们使用了没有对目标领域进行适应的模型。我们的意见书取得了最高的成绩，超过了第二好的球队超过6个百分点。我们的实验证实了在验证的语言模型的新范式中证实了反直觉的结果：给定标记的数据有限，对目标任务的适应性很小或没有适应性的模型比对数据分布的变化更为强大，而不是对其进行微调的模型。代码可从https://github.com/neuralmind-ai/coliee获得。
There has been mounting evidence that pretrained language models fine-tuned on large and diverse supervised datasets can transfer well to a variety of out-of-domain tasks. In this work, we investigate this transfer ability to the legal domain. For that, we participated in the legal case entailment task of COLIEE 2021, in which we use such models with no adaptations to the target domain. Our submissions achieved the highest scores, surpassing the second-best team by more than six percentage points. Our experiments confirm a counter-intuitive result in the new paradigm of pretrained language models: given limited labeled data, models with little or no adaptation to the target task can be more robust to changes in the data distribution than models fine-tuned on it. Code is available at https://github.com/neuralmind-ai/coliee.
