Cryptic crosswords are puzzles that rely not only on general knowledge but
also on the solver's ability to manipulate language on different levels and
deal with various types of wordplay. Previous research suggests that solving
such puzzles is a challenge even for modern NLP models. However, the abilities
of large language models (LLMs) have not yet been tested on this task. In this
paper, we establish the benchmark results for three popular LLMs -- LLaMA2,
Mistral, and ChatGPT -- showing that their performance on this task is still
far from that of humans.

利用三种大型语言模型对神秘填字游戏进行基准测试，结果显示它们在这个任务上的表现远远低于人类。

LLM 是否是出色的密码填字游戏解答者？

Are LLMs Good Cryptic Crossword Solvers?

We extend Textual Inversion to learn pseudo-words that represent a concept at
different resolutions. This allows us to generate images that use the concept
with different levels of detail and also to manipulate different resolutions
using language. Once learned, the user can generate images at different levels
of agreement to the original concept; "A photo of $S^*(0)$" produces the exact
object while the prompt "A photo of $S^*(0.8)$" only matches the rough outlines
and colors. Our framework allows us to generate images that use different
resolutions of an image (e.g. details, textures, styles) as separate
pseudo-words that can be composed in various ways. We open-soure our code in
the following URL: this https URL

本研究将文本反转技术扩展到不同分辨率，学习生成代表概念的伪词，并利用语言进行不同分辨率图像的生成和处理，允许用户根据需要生成不同分辨率的图像，可用于图像生成的多个方面，开源代码位于指定 URL。