[论文解读] Adapting Large Language Models for Education: Foundational Capabilities, Potentials, and Challenges

Qingyao Li, Lingyue Fu+7 more|arXiv (Cornell University)|Dec 27, 2023

Topic Modeling被引用 17

一句话总结

本论文综述了与教育相关的 LLM 能力（数学、写作、编程、推理、问答），并讨论了基于 LLM 的教育系统中统一设计与 MoE（专家混合）设计的取舍。

ABSTRACT

Online education platforms, leveraging the internet to distribute education resources, seek to provide convenient education but often fall short in real-time communication with students. They often struggle to address the diverse obstacles students encounter throughout their learning journey. Solving the problems encountered by students poses a significant challenge for traditional deep learning models, as it requires not only a broad spectrum of subject knowledge but also the ability to understand what constitutes a student's individual difficulties. It's challenging for traditional machine learning models, as they lack the capacity to comprehend students' personalized needs. Recently, the emergence of large language models (LLMs) offers the possibility for resolving this issue by comprehending individual requests. Although LLMs have been successful in various fields, creating an LLM-based education system is still challenging for the wide range of educational skills required. This paper reviews the recently emerged LLM research related to educational capabilities, including mathematics, writing, programming, reasoning, and knowledge-based question answering, with the aim to explore their potential in constructing the next-generation intelligent education system. Specifically, for each capability, we focus on investigating two aspects. Firstly, we examine the current state of LLMs regarding this capability: how advanced they have become, whether they surpass human abilities, and what deficiencies might exist. Secondly, we evaluate whether the development methods for LLMs in this area are generalizable, that is, whether these methods can be applied to construct a comprehensive educational supermodel with strengths across various capabilities, rather than being effective in only a singular aspect.

研究动机与目标

• 评估当前 LLM 在与教育相关的能力上的表现（数学、写作、编程、推理、基于知识的问答）。
• 确定基于 LLM 的教育系统的设计方法（统一模型 vs 专家混合）。
• 突出在智能教育中部署 LLM 的挑战与未来方向。

提出的方法

• 回顾并综合最近在五个领域（数学、写作、编程、推理、问答）上的教育能力相关的 LLM 研究。
• 总结来自公开排行榜（如 OpenCompass、HuggingFace、C-Eval） 的实验发现与基准测试。
• 讨论两种架构方法：单一统一的 LLM 与带有 LLM 控制器的专家混合（Mixture-of-Experts）。

实验结果

研究问题

• RQ1 教育相关任务（数学、写作、编程、推理、问答）方面的 LLM 能力的当前状态如何？
• RQ2 用于 LLM 基础教育系统的架构有哪些可行选项（统一 vs MoE）及其取舍？
• RQ3 阻碍在教育中有效部署 LLM 的关键挑战是什么？
• RQ4 基准测试在模型和能力之间的结果如何变化？
• RQ5 未来方向如何推动使用 LLM 的自适应、智能教育系统？

主要发现

• GPT-4 在受调查的教育基准中总体表现最好。
• LLMs 在 TruthfulQA 上仍落后于人类，表明在事实性和安全性回答方面存在差距。
• 模型强项差异显著；有的在文本理解上表现出色，而在数学和编程方面则表现较差。
• 用于教育系统的两种可行架构是统一模型处理所有任务以及带有 LLM 控制器的专家混合。
• 开放域和领域特定的问答结合检索的方法可以减轻幻觉现象并提升事实基础性。
• 在基准测试中的评估结果显示，没有单一模型在所有能力上都占优势，强调需要专门化或混合系统。