考虑推理错误的机器教学框架，提升少样本学习可靠性。
Teaching and Learning under Deductive Errors
- 引入容错教学框架，允许学习者在推理中出错
- 在修正的PAC框架下证明可找到近似正确假设的教学习集
- 给出教学习集计算的复杂度分析，适合研究大模型教学
现有机器教学与学习模型通常假设学习者不会在内部演绎推理中出错。然而，人类及少样本学习中的大语言模型正是此类错误存在的典型实例，它们可能在一致性检测中失败，且失败具有随机性。本文提出一种考虑推理错误的教学与学习框架，重点研究机器教学情形，因教师的不同刻画可涵盖教学与学习。在改进的可能近似正确（PAC）设置下，我们理论上表明：对于给定的错误水平估计，教师需找到一个教学集，以高概率使学习者推断出近似正确的假设。我们研究了六类与最优PAC教学集计算相关的计算复杂性问题，给出了以教学集大小为参数的XP算法，并在标准复杂性假设（如ETH）下得到紧致的时间复杂度界。实验部分验证了若干教学与学习协议对实际大模型教学会话行为的拟合效果。
原文摘要 · Abstract (English)
Most models of machine teaching and learning assume the learner makes no errors in its internal deductive inference. However, humans and large language models in few-shot learning regimes are two important examples of learners where this does not hold. They fail on some consistency checks, and they can fail stochastically. In this paper we introduce a teaching and learning framework that takes these deductive errors into account. We specifically study the case of machine teaching, as different characterizations of the teacher can account for both machine teaching and learning. In an overhauled Probably Approximately Correct (PAC) setting, we study theoretically that, for some estimated error level, the teacher must find a PAC teaching set that with high probability will lead the learner to guess a hypothesis that is approximately correct. We study the computational complexity of six different problems related to computing optimal PAC teaching sets. We give XP algorithms parametrized by size of teaching set, with tight runtime bounds under standard complexity assumptions like ETH. These results are complemented with a small experimental study of which teaching and learning protocols can best represent the observed behavior in some LLM teaching sessions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。