Publications proposing novel machine learning methods are often primarily
rated by exhibited predictive performance on selected problems. In this
position paper we argue that predictive performance alone is not a good
indicator for the worth of a publication. Using it as such even fosters
problems like inefficiencies of the machine learning research community as a
whole and setting wrong incentives for researchers. We therefore put out a call
for the publication of "negative" results, which can help alleviate some of
these problems and improve the scientific output of the machine learning
research community. To substantiate our position, we present the advantages of
publishing negative results and provide concrete measures for the community to
move towards a paradigm where their publication is normalized.

机器学习方法、预测性能、负面结果、效率低下、科学产出

位置：机器学习中接受负面结果

Position: Embracing Negative Results in Machine Learning

We aim to determine the extent and content of guidance for authors regarding
the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large
Language Models (LLMs) powered tools among the top 100 academic publishers and
journals in science. The websites of these publishers and journals were
screened from between 19th and 20th May 2023. Among the largest 100 publishers,
17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top
25 publishers. Among the top 100 journals, 70% have provided guidance on GAI.
Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited
the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the
use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15
(21.4%) journals indicated their guidance exclusively applies to the writing
process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of
journals included specific disclosure criteria. There was variability in
guidance of where to disclose the use of GAI, including in the methods,
acknowledgments, cover letter, or a new section. There was also variability in
how to access GAI guidance and the linking of journal and publisher
instructions to authors. There is a lack of guidance by some top publishers and
journals on the use of GAI by authors. Among those publishers and journals that
provide guidance, there is substantial heterogeneity in the allowable uses of
GAI and in how it should be disclosed, with this heterogeneity persisting among
affiliated publishers and journals in some instances. The lack of
standardization burdens authors and threatens to limit the effectiveness of
these regulations. There is a need for standardized guidelines in order to
protect the integrity of scientific output as GAI continues to grow in
popularity.

为了确定科学领域前 100 名学术出版社和期刊对于生成 - AI、预训练生成模型和大型语言模型工具的使用向作者提供的指导程度和内容，我们在 2023 年 5 月 19 日至 20 日期间筛选了这些出版社和期刊的网站。在最大的 100 家出版社中，17% 提供了关于生成 - AI 使用的指导，其中 12 家（70.6%）是前 25 名的出版社。在前 100 名期刊中，70% 提供了关于生成 - AI 的指导。在这些指导中，有 94.1% 的出版社和 95.7% 的期刊禁止将生成 - AI 作为作者。有 4 个期刊（5.7%）明确禁止在撰写稿件过程中使用生成 - AI，而 3 个出版社（17.6%）和 15 个期刊（21.4%）则表示他们的指导仅适用于写作过程。在披露生成 - AI 使用时，42.8% 的出版社和 44.3% 的期刊包括了具体的披露标准。关于在哪里披露生成 - AI 的使用存在着不同的指导，包括在方法、致谢、讨论或者新的章节中。关于如何访问生成 - AI 的指导以及期刊和出版社将指导与作者的规定联系起来，也存在着不同的做法。一些顶级出版社和期刊对于作者使用生成 - AI 缺乏指导，指导程度存在着显著的异质性，包括使用范围和披露要求的异质性，这种异质性在一些关联的出版社和期刊中仍然存在。缺乏标准化的指导给作者带来了负担，并可能限制这些规定的效力。为了保护科学成果的完整性，在生成 - AI 不断增长的趋势下，需要制定标准化的指南。

学术和科学出版中关于生成式人工智能的出版商和期刊作者指南的文献计量分析

Bibliometric Analysis of Publisher and Journal Instructions to Authors  on Generative-AI in Academic and Scientific Publishing

The introduction of ChatGPT has fuelled a public debate on the use of
generative AI (large language models; LLMs), including its use by researchers.
In the current work, we test whether delegating parts of the research process
to LLMs leads people to distrust and devalue researchers and scientific output.
Participants (N=402) considered a researcher who delegates elements of the
research process to a PhD student or LLM, and rated (1) moral acceptability,
(2) trust in the scientist to oversee future projects, and (3) the accuracy and
quality of the output. People judged delegating to an LLM as less acceptable
than delegating to a human (d = -0.78). Delegation to an LLM also decreased
trust to oversee future research projects (d = -0.80), and people thought the
results would be less accurate and of lower quality (d = -0.85). We discuss how
this devaluation might transfer into the underreporting of generative AI use.

研究表明将研究流程的一部分委派给生成式人工智能会使人们对研究人员和科学成果产生不信任和贬值，这可能导致生成式人工智能使用的欠报告。

通过生成式 AI 合作创造的研究评估：实验证据

Judgments of research co-created by generative AI: experimental evidence

I propose the index $\hbar$ ("hbar"), defined as the number of papers of an
individual that have citation count larger than or equal to the $\hbar$ of all
coauthors of each paper, as a useful index to characterize the scientific
output of a researcher that takes into account the effect of multiple
coauthorship. The bar is higher for $\hbar$.

提出了指数 hbar，用于表征研究人员的科学产出，其考虑了多人合著的影响，hbar 的值会因合著者的论文引用量而有所变化。