Zero-Shot Commonsense Validation and Reasoning with Large Language Models: An Evaluation on SemEval-2020 Task 4 Dataset

• URL: http://arxiv.org/abs/2502.15810v1
• Date: Wed, 19 Feb 2025 12:40:49 GMT
• Title: Zero-Shot Commonsense Validation and Reasoning with Large Language Models: An Evaluation on SemEval-2020 Task 4 Dataset
• Authors: Rawand Alfugaha, Mohammad AL-Smadi,
• Abstract summary: This study evaluates the performance of Large Language Models (LLMs) on SemEval-2020 Task 4 dataset.<n>The models are tested on two tasks: Task A (Commonsense Validation), where models determine whether a statement aligns with commonsense knowledge, and Task B (Commonsense Explanation)<n>Results indicate that larger models outperform previous models, with LLaMA3-70B achieving the highest accuracy of 98.40% in Task A whereas, lagging behind previous models with 93.40% in Task B.
• Score: 0.16385815610837165
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study evaluates the performance of Large Language Models (LLMs) on SemEval-2020 Task 4 dataset, focusing on commonsense validation and explanation. Our methodology involves evaluating multiple LLMs, including LLaMA3-70B, Gemma2-9B, and Mixtral-8x7B, using zero-shot prompting techniques. The models are tested on two tasks: Task A (Commonsense Validation), where models determine whether a statement aligns with commonsense knowledge, and Task B (Commonsense Explanation), where models identify the reasoning behind implausible statements. Performance is assessed based on accuracy, and results are compared to fine-tuned transformer-based models. The results indicate that larger models outperform previous models and perform closely to human evaluation for Task A, with LLaMA3-70B achieving the highest accuracy of 98.40% in Task A whereas, lagging behind previous models with 93.40% in Task B. However, while models effectively identify implausible statements, they face challenges in selecting the most relevant explanation, highlighting limitations in causal and inferential reasoning.

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