Predicting legal judgments with reliable confidence is paramount for
responsible legal AI applications. While transformer-based deep neural networks
(DNNs) like BERT have demonstrated promise in legal tasks, accurately assessing
their prediction confidence remains crucial. We present a novel Bayesian
approach called BayesJudge that harnesses the synergy between deep learning and
deep Gaussian Processes to quantify uncertainty through Bayesian kernel Monte
Carlo dropout. Our method leverages informative priors and flexible data
modelling via kernels, surpassing existing methods in both predictive accuracy
and confidence estimation as indicated through brier score. Extensive
evaluations of public legal datasets showcase our model's superior performance
across diverse tasks. We also introduce an optimal solution to automate the
scrutiny of unreliable predictions, resulting in a significant increase in the
accuracy of the model's predictions by up to 27\%. By empowering judges and
legal professionals with more reliable information, our work paves the way for
trustworthy and transparent legal AI applications that facilitate informed
decisions grounded in both knowledge and quantified uncertainty.

利用贝叶斯方法和深度高斯过程，我们提出了一种名为 BayesJudge 的新型贝叶斯方法，通过贝叶斯千次采样 dropout 量化预测的不确定性，提高了预测的准确性和置信度评估，从而实现可靠的法律 AI 应用。

BayesJudge：基于贝叶斯核语言建模的法院判决预测中的置信度不确定性

BayesJudge: Bayesian Kernel Language Modelling with Confidence  Uncertainty in Legal Judgment Prediction

Transformer-based deep neural networks have revolutionized the field of
molecular-related prediction tasks by treating molecules as symbolic sequences.
These models have been successfully applied in various organic chemical
applications by pretraining them with extensive compound libraries and
subsequently fine-tuning them with smaller in-house datasets for specific
tasks. However, many conventional methods primarily focus on single molecules,
with limited exploration of pretraining for reactions involving multiple
molecules. In this paper, we propose ReactionT5, a novel model that leverages
pretraining on the Open Reaction Database (ORD), a publicly available
large-scale resource. We further fine-tune this model for yield prediction and
product prediction tasks, demonstrating its impressive performance even with
limited fine-tuning data compared to traditional models. The pre-trained
ReactionT5 model is publicly accessible on the Hugging Face platform.

利用深度神经网络中的基于 Transformer 的方法，在分子相关的预测任务中，通过将分子视为符号序列，解决了多种有机化学应用问题，并利用公开资源 Open Reaction Database (ORD) 对预训练进行了改进，进一步通过有限的微调数据展示了预训练模型的出色性能，ReactionT5 模型的预训练模型在 Hugging Face 平台上公开可用。