While significant work has been done in the field of NLP on vertical
thinking, which involves primarily logical thinking, little work has been done
towards lateral thinking, which involves looking at problems from an
unconventional perspective and defying existing conceptions and notions.
Towards this direction, SemEval 2024 introduces the task of BRAINTEASER, which
involves two types of questions -- Sentence Puzzles and Word Puzzles that defy
conventional common-sense reasoning and constraints. In this paper, we tackle
both types of questions using few-shot prompting on GPT-3.5 and gain insights
regarding the difference in the nature of the two types. Our prompting strategy
placed us 26th on the leaderboard for the Sentence Puzzle and 15th on the Word
Puzzle task.

这篇论文介绍了在 NLP 领域中进行的关于垂直思维的重要工作，随后探讨了对横向思维的研究不足以及 SemEval 2024 引入的 BRAINTEASER 任务，主要涉及句子拼图和词语拼图两种类型的问题，并借助 GPT-3.5 上使用少量提示的方法处理这两种类型的问题，并从中获得了对两种类型问题性质差异的洞见。在 Sentence Puzzle 和 Word Puzzle 任务中，我们的提示策略在排行榜上分别位列第 26 和第 15。

DaVinci 参与 SemEval-2024 第 9 任务：少样本引导 GPT-3.5 进行非常规推理

DaVinci at SemEval-2024 Task 9: Few-shot prompting GPT-3.5 for  Unconventional Reasoning

Knowledge Graphs (KG) are the backbone of many data-intensive applications
since they can represent data coupled with its meaning and context. Aligning
KGs across different domains and providers is necessary to afford a fuller and
integrated representation. A severe limitation of current KG alignment (KGA)
algorithms is that they fail to articulate logical thinking and reasoning with
lexical, structural, and semantic data learning. Deep learning models are
increasingly popular for KGA inspired by their good performance in other tasks,
but they suffer from limitations in explainability, reasoning, and data
efficiency. Hybrid neurosymbolic learning models hold the promise of
integrating logical and data perspectives to produce high-quality alignments
that are explainable and support validation through human-centric approaches.
This paper examines the current state of the art in KGA and explores the
potential for neurosymbolic integration, highlighting promising research
directions for combining these fields.

知识图谱是许多数据密集型应用程序的基础，它们能够表示数据及其含义和上下文。本文通过检查当前知识图谱对齐的现状并探索神经符号集成的潜力，突出结合这些领域的有希望的研究方向。