Large Language Models (LLMs), representing a significant achievement in
artificial intelligence (AI) research, have demonstrated their ability in a
multitude of tasks. This project aims to explore the capabilities of GPT-3.5, a
leading example of LLMs, in processing the sentiment analysis of Internet
memes. Memes, which include both verbal and visual aspects, act as a powerful
yet complex tool for expressing ideas and sentiments, demanding an
understanding of societal norms and cultural contexts. Notably, the detection
and moderation of hateful memes pose a significant challenge due to their
implicit offensive nature. This project investigates GPT's proficiency in such
subjective tasks, revealing its strengths and potential limitations. The tasks
include the classification of meme sentiment, determination of humor type, and
detection of implicit hate in memes. The performance evaluation, using datasets
from SemEval-2020 Task 8 and Facebook hateful memes, offers a comparative
understanding of GPT responses against human annotations. Despite GPT's
remarkable progress, our findings underscore the challenges faced by these
models in handling subjective tasks, which are rooted in their inherent
limitations including contextual understanding, interpretation of implicit
meanings, and data biases. This research contributes to the broader discourse
on the applicability of AI in handling complex, context-dependent tasks, and
offers valuable insights for future advancements.

本研究旨在探索 GPT-3.5 作为大型语言模型在处理互联网迷因情感分析方面的能力，包括迷因情感分类、幽默类型确定和隐性仇恨迷因检测。评估结果表明，尽管 GPT 取得了显著进展，但仍然面临内在限制，包括对上下文的理解、隐含含义的解释和数据偏见等。该研究对 AI 在处理复杂的、依赖上下文的任务方面的适用性进行了探讨，为未来的发展提供了有价值的见解。