GPT-Vision has impressed us on a range of vision-language tasks, but it comes
with the familiar new challenge: we have little idea of its capabilities and
limitations. In our study, we formalize a process that many have instinctively
been trying already to develop "grounded intuition" of this new model. Inspired
by the recent movement away from benchmarking in favor of example-driven
qualitative evaluation, we draw upon grounded theory and thematic analysis in
social science and human-computer interaction to establish a rigorous framework
for qualitative evaluation in natural language processing. We use our technique
to examine alt text generation for scientific figures, finding that GPT-Vision
is particularly sensitive to prompting, counterfactual text in images, and
relative spatial relationships. Our method and analysis aim to help researchers
ramp up their own grounded intuitions of new models while exposing how
GPT-Vision can be applied to make information more accessible.

通过运用扎实理论和主题分析的严谨框架，本研究旨在为自然语言处理提供定性评估方法，以帮助研究人员提高对新模型的扎实理解，并展示 GPT-Vision 在科学图表的替代文本生成方面的应用。

GPT-Vision 在科学图像中的根本直觉

Grounded Intuition of GPT-Vision's Abilities with Scientific Images

In this paper, we offer a preliminary investigation into the task of in-image
machine translation: transforming an image containing text in one language into
an image containing the same text in another language. We propose an end-to-end
neural model for this task inspired by recent approaches to neural machine
translation, and demonstrate promising initial results based purely on
pixel-level supervision. We then offer a quantitative and qualitative
evaluation of our system outputs and discuss some common failure modes.
Finally, we conclude with directions for future work.

本文旨在研究在图像中的机器翻译任务：将包含一种语言文本的图像转化为包含另一种语言文本的图像。我们提出了一个基于神经网络的端到端模型，并证明纯像素级监督可以得到很好的初步结果。我们进行了定量和定性评估，并讨论了一些常见的失误模式。最后，我们提出了未来工作的方向。

面向端到端的图像内神经机器翻译

Towards End-to-End In-Image Neural Machine Translation

E-commerce companies such as Amazon, Alibaba and Flipkart process billions of
orders every year. However, these orders represent only a small fraction of all
plausible orders. Exploring the space of all plausible orders could help us
better understand the relationships between the various entities in an
e-commerce ecosystem, namely the customers and the products they purchase. In
this paper, we propose a Generative Adversarial Network (GAN) for orders made
in e-commerce websites. Once trained, the generator in the GAN could generate
any number of plausible orders. Our contributions include: (a) creating a dense
and low-dimensional representation of e-commerce orders, (b) train an
ecommerceGAN (ecGAN) with real orders to show the feasibility of the proposed
paradigm, and (c) train an ecommerce-conditional-GAN (ec^2GAN) to generate the
plausible orders involving a particular product. We propose several qualitative
methods to evaluate ecGAN and demonstrate its effectiveness. The ec^2GAN is
used for various kinds of characterization of possible orders involving a
product that has just been introduced into the e-commerce system. The proposed
approach ec^2GAN performs significantly better than the baseline in most of the
scenarios.

本文提出了一种基于对抗生成网络的电子商务订单生成方法，通过训练生成器可以产生各种可能的订单，并使用定量和定性方法评估其有效性和适用性。