We propose a comprehensive study of one-stage elicitation techniques for
querying a large pre-trained generative transformer (GPT-3.5-turbo) in the
rhetorical role prediction task of legal cases. This task is known as requiring
textual context to be addressed. Our study explores strategies such as zero-few
shots, task specification with definitions and clarification of annotation
ambiguities, textual context and reasoning with general prompts and specific
questions. We show that the number of examples, the definition of labels, the
presentation of the (labelled) textual context and specific questions about
this context have a positive influence on the performance of the model. Given
non-equivalent test set configurations, we observed that prompting with a few
labelled examples from direct context can lead the model to a better
performance than a supervised fined-tuned multi-class classifier based on the
BERT encoder (weighted F1 score of = 72%). But there is still a gap to reach
the performance of the best systems = 86%) in the LegalEval 2023 task which, on
the other hand, require dedicated resources, architectures and training.

综合研究了一阶段询问大型预训练生成变压器（GPT-3.5-turbo）在法律案件的修辞角色预测任务中的引诱技术，通过零 - 少例、任务说明、文本上下文和问题推理等策略，提出模型性能的改进方案。