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

Contrastive pretrained large Vision-Language Models (VLMs) like CLIP have
revolutionized visual representation learning by providing good performance on
downstream datasets. VLMs are 0-shot adapted to a downstream dataset by
designing prompts that are relevant to the dataset. Such prompt engineering
makes use of domain expertise and a validation dataset. Meanwhile, recent
developments in generative pretrained models like GPT-4 mean they can be used
as advanced internet search tools. They can also be manipulated to provide
visual information in any structure. In this work, we show that GPT-4 can be
used to generate text that is visually descriptive and how this can be used to
adapt CLIP to downstream tasks. We show considerable improvements in 0-shot
transfer accuracy on specialized fine-grained datasets like EuroSAT (~7%), DTD
(~7%), SUN397 (~4.6%), and CUB (~3.3%) when compared to CLIP's default prompt.
We also design a simple few-shot adapter that learns to choose the best
possible sentences to construct generalizable classifiers that outperform the
recently proposed CoCoOP by ~2% on average and by over 4% on 4 specialized
fine-grained datasets. We will release the code, prompts, and auxiliary text
dataset upon acceptance.

我们展示了如何使用 GPT-4 生成视觉描述性文本，并说明如何将其用于适应 CLIP 进行下游任务。与 CLIP 的默认提示相比，在专门的细粒度数据集上，我们在 0-shot 传输准确性方面取得了显著的改进。我们还设计了一个简单的 few-shot 适配器，学习选择最佳的句子来构建具有优越性能的可推广分类器。

用 GPT-4 增强 CLIP：利用视觉描述作为提示

Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts

Prompt tuning has become a new paradigm for model tuning and it has
demonstrated success in natural language pretraining and even vision
pretraining. In this work, we explore the transfer of prompt tuning to
multimodal pretraining, with a focus on generative multimodal pretrained
models, instead of contrastive ones. Specifically, we implement prompt tuning
on the unified sequence-to-sequence pretrained model adaptive to both
understanding and generation tasks. Experimental results demonstrate that the
light-weight prompt tuning can achieve comparable performance with finetuning
and surpass other light-weight tuning methods. Besides, in comparison with
finetuned models, the prompt-tuned models demonstrate improved robustness
against adversarial attacks. We further figure out that experimental factors,
including the prompt length, prompt depth, and reparameteratization, have great
impacts on the model performance, and thus we empirically provide a
recommendation for the setups of prompt tuning. Despite the observed
advantages, we still find some limitations in prompt tuning, and we
correspondingly point out the directions for future studies. Codes are
available at https://github.com/OFA-Sys/OFA

本文探讨将 Prompt 调参应用于多模态预训练，使用基于生成模型的统一序列到序列的预训练模型，实现轻量级 Prompt 调参，并与微调进行比较，通过实验研究发现 Prompt 调参具有改善鲁棒性的优点，但也存在一些局限性，给出了未来研究的方向。