Prior work on language models (LMs) shows that training on a large number of
diverse tasks improves few-shot learning (FSL) performance on new tasks. We
take this to the extreme, automatically extracting 413,299 tasks from internet
tables - orders of magnitude more than the next-largest public datasets.
Finetuning on the resulting dataset leads to improved FSL performance on
Natural Language Processing (NLP) tasks, but not proportionally to dataset
scale. In fact, we find that narrow subsets of our dataset sometimes outperform
more diverse datasets. For example, finetuning on software documentation from
this http URL raises FSL performance by a mean of +7.5% on 52 downstream
tasks, which beats training on 40 human-curated NLP datasets (+6.7%).
Finetuning on various narrow datasets leads to similar broad improvements
across test tasks, suggesting that the gains are not from domain adaptation but
adapting to FSL in general. We do not observe clear patterns between the
datasets that lead to FSL gains, leaving open questions about why certain data
helps with FSL.

该研究发现将预训练语言模型 (LMs) 应用于许多具有多样性任务的训练可提高少量任务学习（FSL）模型的性能。作者从互联网表格中提取了 413,299 个任务并证明其中的一些数据集能更好地用于 NLP 任务的 FSL 模型中。

Few-shot 适应在不确定数据中的应用

Few-shot Adaptation Works with UnpredicTable Data

Continuous evolution in modern software often causes documentation,
tutorials, and examples to be out of sync with changing interfaces and
frameworks. Relying on outdated documentation and examples can lead programs to
fail or be less efficient or even less secure. In response, programmers need to
regularly turn to other resources on the web such as StackOverflow for examples
to guide them in writing software. We recognize that this inconvenient,
error-prone, and expensive process can be improved by using machine learning
applied to software usage data. In this paper, we present our practical system
which uses machine learning on large-scale telemetry data and documentation
corpora, generating appropriate and complex examples that can be used to
improve documentation. We discuss both feature-based and transformer-based
machine learning approaches and demonstrate that our system achieves 100%
coverage for the used functionalities in the product, providing up-to-date
examples upon every release and reduces the numbers of PRs submitted by
software owners writing and editing documentation by >68%. We also share
valuable lessons learnt during the 3 years that our production quality system
has been deployed for Azure Cloud Command Line Interface (Azure CLI).

本文介绍了一种用于改善软件文档的实用系统，通过机器学习应用于软件使用数据和文档资料库来产生适当且复杂的示例，实现了对产品中使用的功能的 100％覆盖率，并在每次发布时提供最新的示例，减少了软件所有者撰写和编辑文档时提交 PR 的数量超过 68％。

CLI 使用示例的生成：转换器可行吗？

Generating Examples From CLI Usage: Can Transformers Help?

Source Code Summarization is the task of writing short, natural language
descriptions of source code. The main use for these descriptions is in software
documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code
summarization is rapidly becoming a popular research problem, but progress is
restrained due to a lack of suitable datasets. In addition, a lack of community
standards for creating datasets leads to confusing and unreproducible research
results -- we observe swings in performance of more than 33% due only to
changes in dataset design. In this paper, we make recommendations for these
standards from experimental results. We release a dataset based on prior work
of over 2.1m pairs of Java methods and one sentence method descriptions from
over 28k Java projects. We describe the dataset and point out key differences
from natural language data, to guide and support future researchers.

该研究提出了如何通过实验结果规范化源代码生成简要自然语言描述的数据集以及对其的关键差异进行指导和支持的方法，主要应用于软件文档中的代码摘要生成，促进代码摘要生成领域的发展。