LLM在上下文学习中偏好简单概念，复杂度影响表现。
Minimization of Boolean Complexity in In-Context Concept Learning
- 用布尔复杂度衡量概念难易，测试模型学习表现
- 概念布尔复杂度越高，模型准确率越低，相关性显著
- 揭示模型与人类相似的简约学习偏好，适合认知研究者
大型语言模型（LLMs）在上下文学习中的相对成功与困难受何因素影响？基于人类概念学习的文献洞见，我们设计了精细的概念学习任务，对LLMs进行测试，发现任务表现与概念的布尔复杂度高度相关。这表明，上下文学习表现出一种类似人类的、对简单的学习偏好。该研究为理解大模型的认知机制提供了新视角。
原文摘要 · Abstract (English)
What factors contribute to the relative success and corresponding difficulties of in-context learning for Large Language Models (LLMs)? Drawing on insights from the literature on human concept learning, we test LLMs on carefully designed concept learning tasks, and show that task performance highly correlates with the Boolean complexity of the concept. This suggests that in-context learning exhibits a learning bias for simplicity in a way similar to humans.
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