Motivated by the large progress made by large language models (LLMs), we
introduce the framework of verbalized machine learning (VML). In contrast to
conventional machine learning models that are typically optimized over a
continuous parameter space, VML constrains the parameter space to be
human-interpretable natural language. Such a constraint leads to a new
perspective of function approximation, where an LLM with a text prompt can be
viewed as a function parameterized by the text prompt. Guided by this
perspective, we revisit classical machine learning problems, such as regression
and classification, and find that these problems can be solved by an
LLM-parameterized learner and optimizer. The major advantages of VML include
(1) easy encoding of inductive bias: prior knowledge about the problem and
hypothesis class can be encoded in natural language and fed into the
LLM-parameterized learner; (2) automatic model class selection: the optimizer
can automatically select a concrete model class based on data and verbalized
prior knowledge, and it can update the model class during training; and (3)
interpretable learner updates: the LLM-parameterized optimizer can provide
explanations for why each learner update is performed. We conduct several
studies to empirically evaluate the effectiveness of VML, and hope that VML can
serve as a stepping stone to stronger interpretability and trustworthiness in
ML.

我们引入了口头化机器学习 (VML) 框架，通过限制参数空间为人类可解释的自然语言，将大型语言模型 (LLMs) 视为由文本提示参数化的函数，通过 VML，我们重新审视经典的机器学习问题，如回归和分类，发现这些问题可以通过 LLM 参数化的学习器和优化器来解决，VML 的主要优势包括容易编码归纳偏差、自动模型选择和解释性学习器更新，我们进行了多项研究来评估 VML 的有效性，并希望 VML 能成为机器学习中更强解释能力和可信度的垫脚石。