We report the presence of a simple neural mechanism that represents an
input-output function as a vector within autoregressive transformer language
models (LMs). Using causal mediation analysis on a diverse range of
in-context-learning (ICL) tasks, we find that a small number attention heads
transport a compact representation of the demonstrated task, which we call a
function vector (FV). FVs are robust to changes in context, i.e., they trigger
execution of the task on inputs such as zero-shot and natural text settings
that do not resemble the ICL contexts from which they are collected. We test
FVs across a range of tasks, models, and layers and find strong causal effects
across settings in middle layers. We investigate the internal structure of FVs
and find while that they often contain information that encodes the output
space of the function, this information alone is not sufficient to reconstruct
an FV. Finally, we test semantic vector composition in FVs, and find that to
some extent they can be summed to create vectors that trigger new complex
tasks. Taken together, our findings suggest that LLMs contain internal
abstractions of general-purpose functions that can be invoked in a variety of
contexts.

通过对多样的上下文学习任务的因果中介分析，我们发现一种简单的神经机制，在自回归 Transformer 语言模型中将输入输出函数表示为向量，这种向量被我们称为函数向量（Function Vector）。函数向量（FV）对上下文的变化具有鲁棒性，在与上下文不相似的输入（如零 - shot 和自然文本设置）上触发任务的执行。我们测试了函数向量在多种任务、模型和层次上的效果，并发现在中间层次的不同设置中具有强大的因果效应。我们研究了函数向量的内部结构，并发现虽然它们经常包含编码函数的输出空间的信息，但仅有这些信息并不足以重构一个函数向量。最后，我们测试了函数向量中语义向量的组合，并发现在一定程度上它们可以被相加以创建触发新复杂任务的向量。综上所述，我们的研究结果表明，语言模型中包含了内部的抽象的通用函数，可以在各种不同的上下文中被调用。