迭代前向调优提升语言模型中的上下文学习
Iterative Forward Tuning Boosts In-Context Learning in Language Models
AI总结：
提出两阶段Deep-Thinking框架，通过无需训练的迭代增强注意力机制多次处理示例，显著提升LLMs的上下文学习效果。
摘要：
尽管大型语言模型（LLMs）在上下文学习（ICL）方面取得了进展，但当前研究主要集中在特定的提示工程上，例如示例选择，并期望单次迭代的示例处理能够有效地泛化到给定的测试样本。然而，这种观点忽略了涉及示例的多次迭代可能带来的潜在益处，这种做法更贴近人类通过类比进行学习的迭代决策过程。在本研究中，我们引入了一个新颖的两阶段框架来提升LLMs中的ICL。具体而言，我们的框架将ICL过程划分为两个不同阶段：Deep-Thinking阶段和测试阶段。Deep-Thinking阶段结合了一种独特的注意力机制，即迭代增强注意力，它允许多轮信息积累。该机制通过在无需训练的情况下操纵Key-Value矩阵来运作，通过多次思考示例来培养LLMs更强的理解能力。我们在一系列基准测试和LLMs上评估了Deep-Thinking，展示了其优于普通ICL方法的性能，以及在示例选择不可行的挑战性任务中的有效性。
英文摘要：
Despite the advancements in in-context learning (ICL) for large language models (LLMs), current research centers on specific prompt engineering, such as demonstration selection, with the expectation that a single iteration of demonstrations processing can generalize effectively to a given test sample. However, this perspective overlooks the potential benefits derived from multiple iterations involving demonstrations, a practice aligning more closely with the iterative decision-making process exhibited by humans, who often learn through analogy. In this study, we introduce a novel two-stage framework to boost ICL in LLMs. Specifically, our framework delineates the ICL process into two distinct stages: Deep-Thinking and test stages. The Deep-Thinking stage incorporates a unique attention mechanism, i.e., iterative enhanced attention, which enables multiple rounds of information accumulation. This mechanism operates by manipulating the Key-Value matrices without training, fostering enhanced understanding capabilities in LLMs by thinking demonstrations multiple times. We evaluated Deep-Thinking across a range of benchmarks and LLMs, showing its superior performance over vanilla ICL methods and its effectiveness in challenging tasks where demonstration selection is infeasible.
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。
