标题： (WhyPHI) 为多项选择题回答微调PHI-3：方法、结果和挑战 显示英文标题

标题： (WhyPHI) Fine-Tuning PHI-3 for Multiple-Choice Question Answering: Methodology, Results, and Challenges

摘要： 大型语言模型（LLMs）由于其在理解和生成类人文本方面的出色能力，已成为各个领域的重要工具。准确回答选择题（MCQs）的能力在教育领域具有重要意义，特别是在自动辅导系统和评估平台中。然而，由于幻觉和模糊的提示，使LLMs有效处理MCQ任务仍然具有挑战性。这项工作探讨了微软的PHI-3\cite{Abdin2024}这种紧凑而高效的LLM在回答选择题方面的潜力。我们的贡献包括在TruthfulQA数据集上微调模型，设计优化的提示以提高模型性能，并使用困惑度和传统指标如准确率和F1分数进行评估。结果表明，微调后PHI-3.5在处理选择题方面有显著改进，困惑度从4.68降至2.27，准确率从62%升至90.8%。这项研究强调了高效模型在自适应学习系统和教育评估中的重要性，为更广泛地融入课堂铺平了道路，特别是在考试准备、学生反馈和个性化学习等领域。 显示英文摘要

摘要： Large Language Models (LLMs) have become essential tools across various domains due to their impressive capabilities in understanding and generating human-like text. The ability to accurately answer multiple-choice questions (MCQs) holds significant value in education, particularly in automated tutoring systems and assessment platforms. However, adapting LLMs to handle MCQ tasks effectively remains challenging due to the hallucinations and unclear prompts. This work explores the potential of Microsoft's PHI-3\cite{Abdin2024}, a compact yet efficient LLM, for MCQ answering. Our contributions include fine-tuning the model on the TruthfulQA dataset, designing optimized prompts to enhance model performance, and evaluating using perplexity and traditional metrics like accuracy and F1 score. Results show a remarkable improvement in PHI-3.5's MCQ handling post-fine-tuning, with perplexity decreasing from 4.68 to 2.27, and accuracy rising from 62\% to 90.8\%. This research underlines the importance of efficient models in adaptive learning systems and educational assessments, paving the way for broader integration into the classroom, particularly in fields like test preparation, student feedback, and personalized learning.