测试ChatGPT通过基于ASCII艺术的多模态任务的理解深度：GPT3.5在识别和生成ASCII艺术方面的能力并非完全缺失
Testing the Depth of ChatGPT's Comprehension via Cross-Modal Tasks Based on ASCII-Art: GPT3.5's Abilities in Regard to Recognizing and Generating ASCII-Art Are Not Totally Lacking
AI总结：
本研究通过ASCII艺术视觉任务测试GPT3.5的理解深度，发现其在识别和生成ASCII艺术方面并非完全无能，揭示了多模态能力的局限与潜力。
摘要：
自发布以来的八个月里，ChatGPT及其底层模型GPT3.5因其能力与可访问性的强大结合而获得了大量关注。虽然已经出现了一批专门研究这些模型能力范围的论文，但输入和提取自这些网络的信息要么是自然语言文本，要么是风格化的类代码语言。受我们对真正人类级智能体在多信号模态中应具备的卓越能力的启发，在本工作中，我们考察了GPT3.5在视觉任务上的能力，其中输入以ASCII艺术形式提供内容，而未经显式提炼为语言摘要。我们进行了实验，分析了模型在视觉环境中典型的各种变换后的图像识别任务上的表现，测试了其对图像组成部分的知识，以及涵盖图像生成的任务。
英文摘要：
Over the eight months since its release, ChatGPT and its underlying model, GPT3.5, have garnered massive attention, due to their potent mix of capability and accessibility. While a niche-industry of papers have emerged examining the scope of capabilities these models possess, the information fed to and extracted from these networks has been either natural language text or stylized, code-like language. Drawing inspiration from the prowess we expect a truly human-level intelligent agent to have across multiple signal modalities, in this work we examine GPT3.5's aptitude for visual tasks, where the inputs feature content provided as ASCII-art without overt distillation into a lingual summary. We conduct experiments analyzing the model's performance on image recognition tasks after various transforms typical in visual settings, trials investigating knowledge of image parts, and tasks covering image generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
