Interpretability research aims to build tools for understanding machine
learning (ML) models. However, such tools are inherently hard to evaluate
because we do not have ground truth information about how ML models actually
work. In this work, we propose to build transformer models manually as a
testbed for interpretability research. We introduce Tracr, a "compiler" for
translating human-readable programs into weights of a transformer model. Tracr
takes code written in RASP, a domain-specific language (Weiss et al. 2021), and
translates it into weights for a standard, decoder-only, GPT-like transformer
architecture. We use Tracr to create a range of ground truth transformers that
implement programs including computing token frequencies, sorting, and Dyck-n
parenthesis checking, among others. To enable the broader research community to
explore and use compiled models, we provide an open-source implementation of
Tracr at this https URL.

在解释性研究中，我们提出使用手动构建的 transformer 模型作为可解释性测试平台，通过 Tracr 将人类可读的程序翻译为 transformer 模型的权重，从而得到实现包括计算令牌频率，排序，Dyck-n 括号验证等程序的多个 ground truth transformers。