标题： ICLR：表示的上下文学习 显示英文标题

标题： ICLR: In-Context Learning of Representations

摘要： 近期的研究表明，预训练数据指定的语义会影响大型语言模型（LLM）中不同概念表征的组织方式。然而，考虑到LLM的开放性特性，比如它们的即时学习能力，我们可以问这些模型是否会改变这些预训练语义，以采用替代性的、由上下文指定的语义。具体来说，如果我们提供一些即时示例，其中某个概念扮演的角色与预训练数据所暗示的不同，模型是否会根据这些新的语义重新组织它们的表征？为了回答这个问题，我们受到概念角色语义理论的启发，并定义了一个玩具“图追踪”任务，在这个任务中，图的节点通过训练过程中看到的概念（例如苹果、鸟等）来引用，而图的连通性则通过某种预定义的结构（例如正方形网格）来定义。给定指示图上随机游走轨迹的示例，我们分析了模型的中间表征，并发现随着上下文量的增加，存在从预训练语义表征到与图结构对齐的即时上下文表征的突然重组。此外，我们发现当参考概念在其语义上有相关性时（例如星期一、星期二等），由上下文指定的图结构仍然存在于表征中，但无法主导预训练结构。为了解释这些结果，我们将我们的任务类比为预先定义的图拓扑的能量最小化过程，提供了关于隐式优化过程推断上下文指定语义的证据。总体而言，我们的研究结果表明，扩展上下文大小可以灵活地重组模型表征，可能解锁新的能力。 显示英文摘要

摘要： Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However, given the open-ended nature of LLMs, e.g., their ability to in-context learn, we can ask whether models alter these pretraining semantics to adopt alternative, context-specified ones. Specifically, if we provide in-context exemplars wherein a concept plays a different role than what the pretraining data suggests, do models reorganize their representations in accordance with these novel semantics? To answer this question, we take inspiration from the theory of conceptual role semantics and define a toy "graph tracing" task wherein the nodes of the graph are referenced via concepts seen during training (e.g., apple, bird, etc.) and the connectivity of the graph is defined via some predefined structure (e.g., a square grid). Given exemplars that indicate traces of random walks on the graph, we analyze intermediate representations of the model and find that as the amount of context is scaled, there is a sudden re-organization from pretrained semantic representations to in-context representations aligned with the graph structure. Further, we find that when reference concepts have correlations in their semantics (e.g., Monday, Tuesday, etc.), the context-specified graph structure is still present in the representations, but is unable to dominate the pretrained structure. To explain these results, we analogize our task to energy minimization for a predefined graph topology, providing evidence towards an implicit optimization process to infer context-specified semantics. Overall, our findings indicate scaling context-size can flexibly re-organize model representations, possibly unlocking novel capabilities.