(Source) code summarization is the task of automatically generating natural
language summaries for given code snippets. Such summaries play a key role in
helping developers understand and maintain source code. Recently, with the
successful application of large language models (LLMs) in numerous fields,
software engineering researchers have also attempted to adapt LLMs to solve
code summarization tasks. The main adaptation schemes include instruction
prompting and task-oriented fine-tuning. However, instruction prompting
involves designing crafted prompts for zero-shot learning or selecting
appropriate samples for few-shot learning and requires users to have
professional domain knowledge, while task-oriented fine-tuning requires high
training costs. In this paper, we propose a novel prompt learning framework for
code summarization called PromptCS. PromptCS trains a prompt agent that can
generate continuous prompts to unleash the potential for LLMs in code
summarization. Compared to the human-written discrete prompt, the continuous
prompts are produced under the guidance of LLMs and are therefore easier to
understand by LLMs. PromptCS freezes the parameters of LLMs when training the
prompt agent, which can greatly reduce the requirements for training resources.
We evaluate PromptCS on the CodeSearchNet dataset involving multiple
programming languages. The results show that PromptCS significantly outperforms
instruction prompting schemes on all four widely used metrics. In some base
LLMs, e.g., CodeGen-Multi-2B and StarCoderBase-1B and -3B, PromptCS even
outperforms the task-oriented fine-tuning scheme. More importantly, the
training efficiency of PromptCS is faster than the task-oriented fine-tuning
scheme, with a more pronounced advantage on larger LLMs. The results of the
human evaluation demonstrate that PromptCS can generate more good summaries
compared to baselines.

本文提出了一种用于代码摘要的新型提示学习框架，称为 PromptCS，该框架训练了一个能够生成连续提示的提示代理，以释放大语言模型在代码摘要中的潜力，相比于人工编写的离散提示，连续提示更易于大语言模型理解。PromptCS 在 CodeSearchNet 数据集上的评估结果表明，优于所有四个广泛使用的度量标准的指导学习方案，并且在某些基础大语言模型上，例如 CodeGen-Multi-2B 和 StarCoderBase-1B 和 - 3B，甚至优于任务导向的微调方案。重要的是，PromptCS 的训练效率比任务导向的微调方案更快，在较大的大语言模型上具有更明显的优势。人工评估的结果表明，与基准相比，PromptCS 能够生成更好的摘要。

源代码摘要的提示学习框架

A Prompt Learning Framework for Source Code Summarization

Federated weather forecasting is a promising collaborative learning framework
for analyzing meteorological data across participants from different countries
and regions, thus embodying a global-scale real-time weather data predictive
analytics platform to tackle climate change. This paper is to model the
meteorological data in a federated setting where many distributed low-resourced
sensors are deployed in different locations. Specifically, we model the
spatial-temporal weather data into a federated prompt learning framework that
leverages lightweight prompts to share meaningful representation and structural
knowledge among participants. Prompts-based communication allows the server to
establish the structural topology relationships among participants and further
explore the complex spatial-temporal correlations without transmitting private
data while mitigating communication overhead. Moreover, in addition to a
globally shared large model at the server, our proposed method enables each
participant to acquire a personalized model that is highly customized to tackle
climate changes in a specific geographic area. We have demonstrated the
effectiveness of our method on classical weather forecasting tasks by utilizing
three spatial-temporal multivariate time-series weather data.

通过搭建一个基于联邦学习的时空气象数据预测分析框架，本研究在一个去中心化的环境下建立了一个空间 - 时间的气象数据模型，实现了低成本、高效率、安全共享的监督学习和知识融合。在共享大型的融合模型同时，每个参与者还可根据不同的地理位置获取个性化的定制模型来应对气候变化。实验结果表明，该方法可以有效地优化经典气象预测任务。