Large Language Models (LLMs) for code are a family of high-parameter,
transformer-based neural networks pre-trained on massive datasets of both
natural and programming languages. These models are rapidly being employed in
commercial AI-based developer tools, such as GitHub CoPilot. However, measuring
and explaining their effectiveness on programming tasks is a challenging
proposition, given their size and complexity. The methods for evaluating and
explaining LLMs for code are inextricably linked. That is, in order to explain
a model's predictions, they must be reliably mapped to fine-grained,
understandable concepts. Once this mapping is achieved, new methods for
detailed model evaluations are possible. However, most current explainability
techniques and evaluation benchmarks focus on model robustness or individual
task performance, as opposed to interpreting model predictions.
To this end, this paper introduces ASTxplainer, an explainability method
specific to LLMs for code that enables both new methods for LLM evaluation and
visualizations of LLM predictions that aid end-users in understanding model
predictions. At its core, ASTxplainer provides an automated method for aligning
token predictions with AST nodes, by extracting and aggregating normalized
model logits within AST structures. To demonstrate the practical benefit of
ASTxplainer, we illustrate the insights that our framework can provide by
performing an empirical evaluation on 12 popular LLMs for code using a curated
dataset of the most popular GitHub projects. Additionally, we perform a user
study examining the usefulness of an ASTxplainer-derived visualization of model
predictions aimed at enabling model users to explain predictions. The results
of these studies illustrate the potential for ASTxplainer to provide insights
into LLM effectiveness, and aid end-users in understanding predictions.

ASTxplainer 是针对代码 LLMs 的一种可解释性方法，不仅能提供 LLM 评估的新方法，还能通过可视化 LLM 预测结果帮助终端用户理解模型预测。通过在常用的 GitHub 项目上进行实证评估和用户研究，研究结果表明 ASTxplainer 有潜力深入研究 LLM 的效能，并帮助终端用户理解预测结果。

使用句法结构评估和解释大型语言模型在代码中的应用

Evaluating and Explaining Large Language Models for Code Using Syntactic  Structures

In recent years, Large Language Models (LLMs) have gained significant
popularity due to their ability to generate human-like text and their potential
applications in various fields, such as Software Engineering. LLMs for Code are
commonly trained on large unsanitized corpora of source code scraped from the
Internet. The content of these datasets is memorized and emitted by the models,
often in a verbatim manner. In this work, we will discuss the security,
privacy, and licensing implications of memorization. We argue why the use of
copyleft code to train LLMs is a legal and ethical dilemma. Finally, we provide
four actionable recommendations to address this issue.

本研究探讨使用未经授权的代码构建 LLMs 所带来的安全、隐私和版权问题，并提出了四条可行的建议。