大语言模型中的涌现能力：一项综述
Emergent Abilities in Large Language Models: A Survey
AI总结：
本综述全面梳理大语言模型（LLMs）及大推理模型（LRMs）的涌现能力，分析其定义、出现条件，同时指出其潜在有害行为，强调需完善评估框架与监管监督。
摘要：
大语言模型（Large Language Models, LLMs）作为通往通用人工智能最具前景的研究方向之一，正引领一场新的技术革命。这类模型通过增加参数数量和训练数据集规模实现的缩放，与各种此前未被观测到的所谓“涌现能力”相关联。这些涌现能力涵盖从高级推理、上下文学习到编码和问题解决等多个方面，引发了激烈的科学辩论：它们是真正涌现的，还是仅仅依赖于训练动态、问题类型或所选指标等外部因素？导致它们出现的潜在机制是什么？尽管涌现能力具有变革性潜力，但人们对其仍知之甚少，导致对其定义、本质、可预测性及影响存在误解。在本研究中，我们通过对该现象进行全面综述，阐明涌现能力，探讨其科学基础和现实影响。我们首先批判性分析现有定义，揭示涌现能力概念化中的不一致性；随后探究这些能力出现的条件，评估缩放定律、任务复杂性、预训练损失、量化和提示策略的作用。我们的综述不仅涵盖传统LLMs，还包括利用强化学习和推理时搜索来增强推理与自我反思的大推理模型（Large Reasoning Models, LRMs）。不过，涌现并非本质上是正面的：随着AI系统获得自主推理能力，它们也会产生欺骗、操纵和奖励黑客等有害行为。我们强调对安全与治理日益增长的担忧，强调需要更好的评估框架和监管监督。
英文摘要：
Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these models, accomplished by increasing the number of parameters and the magnitude of the training datasets, has been linked to various so-called emergent abilities that were previously unobserved. These emergent abilities, ranging from advanced reasoning and in-context learning to coding and problem-solving, have sparked an intense scientific debate: Are they truly emergent, or do they simply depend on external factors, such as training dynamics, the type of problems, or the chosen metric? What underlying mechanism causes them? Despite their transformative potential, emergent abilities remain poorly understood, leading to misconceptions about their definition, nature, predictability, and implications. In this work, we shed light on emergent abilities by conducting a comprehensive review of the phenomenon, addressing both its scientific underpinnings and real-world consequences. We first critically analyze existing definitions, exposing inconsistencies in conceptualizing emergent abilities. We then explore the conditions under which these abilities appear, evaluating the role of scaling laws, task complexity, pre-training loss, quantization, and prompting strategies. Our review extends beyond traditional LLMs and includes Large Reasoning Models (LRMs), which leverage reinforcement learning and inference-time search to amplify reasoning and self-reflection. However, emergence is not inherently positive. As AI systems gain autonomous reasoning capabilities, they also develop harmful behaviors, including deception, manipulation, and reward hacking. We highlight growing concerns about safety and governance, emphasizing the need for better evaluation frameworks and regulatory oversight.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
