uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?

• URL: http://arxiv.org/abs/2404.02474v1
• Date: Wed, 3 Apr 2024 05:31:59 GMT
• Title: uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
• Authors: Pouya Sadeghi, Amirhossein Abaskohi, Yadollah Yaghoobzadeh,
• Abstract summary: We investigate how different prompting methods enhance LLMs' performance on a task to reveal their inherent power for outside-the-box thinking ability. We generate a dataset of thinking paths between riddles and options using GPT-4, validated by humans for quality. Findings indicate that compressed informative prompts enhance performance significantly.
• Score: 7.0546788281657875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box. Building upon this benchmark, we investigate how different prompting methods enhance LLMs' performance on this task to reveal their inherent power for outside-the-box thinking ability. Through participating in SemEval-2024, task 9, Sentence Puzzle sub-task, we explore prompt engineering methods: chain of thoughts (CoT) and direct prompting, enhancing with informative descriptions, and employing contextualizing prompts using a retrieval augmented generation (RAG) pipeline. Our experiments involve three LLMs including GPT-3.5, GPT-4, and Zephyr-7B-beta. We generate a dataset of thinking paths between riddles and options using GPT-4, validated by humans for quality. Findings indicate that compressed informative prompts enhance performance. Dynamic in-context learning enhances model performance significantly. Furthermore, fine-tuning Zephyr on our dataset enhances performance across other commonsense datasets, underscoring the value of innovative thinking.

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