大型语言模型中涌现的符号机制支持抽象推理
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
AI总结：
通过分析LLM内部机制，发现其抽象推理依赖由符号抽象、符号归纳和检索三个计算阶段构成的涌现符号架构，为符号与神经网络之争提供了新证据。
摘要：
许多近期研究发现大型语言模型（LLMs）具有涌现的推理能力，但关于这些能力的稳健性以及它们在多大程度上依赖于结构化推理机制的争论仍在持续。为了阐明这些问题，我们研究了支持LLMs中抽象推理的内部机制。我们识别出一种涌现的符号架构，该架构通过一系列三个计算步骤实现抽象推理。在早期层中，符号抽象头根据输入token之间的关系将输入token转换为抽象变量。在中间层中，符号归纳头对这些抽象变量执行序列归纳。最后，在后期层中，检索头通过检索与预测的抽象变量相关联的值来预测下一个token。这些结果指向了符号方法与神经网络方法之间长期争论的一种解决方案，表明神经网络中的涌现推理依赖于符号机制的涌现。
英文摘要：
Many recent studies have found evidence for emergent reasoning capabilities in large language models (LLMs), but debate persists concerning the robustness of these capabilities, and the extent to which they depend on structured reasoning mechanisms. To shed light on these issues, we study the internal mechanisms that support abstract reasoning in LLMs. We identify an emergent symbolic architecture that implements abstract reasoning via a series of three computations. In early layers, symbol abstraction heads convert input tokens to abstract variables based on the relations between those tokens. In intermediate layers, symbolic induction heads perform sequence induction over these abstract variables. Finally, in later layers, retrieval heads predict the next token by retrieving the value associated with the predicted abstract variable. These results point toward a resolution of the longstanding debate between symbolic and neural network approaches, suggesting that emergent reasoning in neural networks depends on the emergence of symbolic mechanisms.
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