TypeScript and Python are two programming languages that support optional
type annotations, which are useful but tedious to introduce and maintain. This
has motivated automated type prediction: given an untyped program, produce a
well-typed output program. Large language models (LLMs) are promising for type
prediction, but there are challenges: fill-in-the-middle performs poorly,
programs may not fit into the context window, generated types may not type
check, and it is difficult to measure how well-typed the output program is. We
address these challenges by building OpenTau, a search-based approach for type
prediction that leverages large language models. We propose a new metric for
type prediction quality, give a tree-based program decomposition that searches
a space of generated types, and present fill-in-the-type fine-tuning for LLMs.
We evaluate our work with a new dataset for TypeScript type prediction, and
show that 47.4% of files type check (14.5% absolute improvement) with an
overall rate of 3.3 type errors per file. All code, data, and models are
available at: this https URL

OpenTau 为解决基于 LLMs 进行自动化类型预测中的问题而构建了一个基于搜索的方法，包括提出了一个新的衡量类型预测质量的度量方式、给出了一种基于树形程序分解的生成类型搜索空间的方法以及针对 LLMs 的 fill-in-the-type 微调方法。在新的 TypeScript 类型预测数据集上评估我们的工作，结果表明，在所有文件中有 47.4% 的文件通过类型检查（相对改善 14.5%），每个文件的总体错误率为 3.3 个类型错误。