Customized medical prompts enable Large Language Models (LLM) to effectively
address medical dialogue summarization. The process of medical reporting is
often time-consuming for healthcare professionals. Implementing medical
dialogue summarization techniques presents a viable solution to alleviate this
time constraint by generating automated medical reports. The effectiveness of
LLMs in this process is significantly influenced by the formulation of the
prompt, which plays a crucial role in determining the quality and relevance of
the generated reports. In this research, we used a combination of two distinct
prompting strategies, known as shot prompting and pattern prompting to enhance
the performance of automated medical reporting. The evaluation of the automated
medical reports is carried out using the ROUGE score and a human evaluation
with the help of an expert panel. The two-shot prompting approach in
combination with scope and domain context outperforms other methods and
achieves the highest score when compared to the human reference set by a
general practitioner. However, the automated reports are approximately twice as
long as the human references, due to the addition of both redundant and
relevant statements that are added to the report.

通过使用两种不同的提示策略（即 shot 提示和 pattern 提示），该研究通过结合范围和领域上下文，改善了自动医学报告的性能，并且获得了最高分数，超过了一般执业医生的参考文献。然而，由于报告中添加了冗余和相关陈述，自动报告的长度大约是人工参考的两倍。

通过基于 Transformer 的提示工程提升自动医疗报告的摘要性能

Enhancing Summarization Performance through Transformer-Based Prompt  Engineering in Automated Medical Reporting

Recent progress in generative models, especially in text-guided diffusion
models, has enabled the production of aesthetically-pleasing imagery resembling
the works of professional human artists. However, one has to carefully compose
the textual description, called the prompt, and augment it with a set of
clarifying keywords. Since aesthetics are challenging to evaluate
computationally, human feedback is needed to determine the optimal prompt
formulation and keyword combination. In this paper, we present a
human-in-the-loop approach to learning the most useful combination of prompt
keywords using a genetic algorithm. We also show how such an approach can
improve the aesthetic appeal of images depicting the same descriptions.

本研究使用基因算法，结合人类反馈，学习组合最有用的提示关键字，以改善使用描述生成艺术图像的美感。

文本到图像模型的最佳提示及其发现方法

Best Prompts for Text-to-Image Models and How to Find Them

Within textual emotion classification, the set of relevant labels depends on
the domain and application scenario and might not be known at the time of model
development. This conflicts with the classical paradigm of supervised learning
in which the labels need to be predefined. A solution to obtain a model with a
flexible set of labels is to use the paradigm of zero-shot learning as a
natural language inference task, which in addition adds the advantage of not
needing any labeled training data. This raises the question how to prompt a
natural language inference model for zero-shot learning emotion classification.
Options for prompt formulations include the emotion name anger alone or the
statement "This text expresses anger". With this paper, we analyze how
sensitive a natural language inference-based zero-shot-learning classifier is
to such changes to the prompt under consideration of the corpus: How carefully
does the prompt need to be selected? We perform experiments on an established
set of emotion datasets presenting different language registers according to
different sources (tweets, events, blogs) with three natural language inference
models and show that indeed the choice of a particular prompt formulation needs
to fit to the corpus. We show that this challenge can be tackled with
combinations of multiple prompts. Such ensemble is more robust across corpora
than individual prompts and shows nearly the same performance as the individual
best prompt for a particular corpus.

本研究通过分析不同的提示公式，以及使用基于自然语言推理的分类器，探讨零样本学习情感分类问题。并且通过使用多个提示公式的组合，实现了更加稳定且表现良好的情感分类。