CAREER: Detecting, Understanding, and Fixing Vulnerabilities in Natural Language Processing Models

随着机器学习的最新进展，模型在自然语言处理（NLP）中的许多具有挑战性的任务（如问答，机器翻译和对话代理）中实现了高准确性，有时接近或击败人类在这些基准上的表现。然而，这些NLP模型通常在许多不同的方面存在脆弱性：它们锁定错误的工件，不支持语言的自然变化，对对抗性攻击不鲁棒，并且只适用于少数几个领域。现有的开发NLP模型的管道缺乏对有用见解的支持，识别错误需要机器学习和该领域专家的大量努力。这个CAREER项目开发了几种技术来支持对NLP更强大的培训和评估管道的需求，为识别，理解和解决NLP模型的漏洞提供易于使用，可扩展和准确的机制。所开发的方法将支持不同的应用领域，如会话代理，情感分类器和滥用/仇恨言论检测。此外，该团队还与学术界和工业界的NLP模型开发人员合作，为K-12教育开发数据科学课程，特别是针对来自代表性不足社区的学生。基于脆弱性是某些输入转换的意外行为的概念，该团队将在以下三个方面做出贡献。第一个推力通过测试用户定义的行为和搜索许多可能的漏洞来识别漏洞。在第二个方面，研究人员开发了通过跟踪错误原因到单个训练数据点和数据工件来理解模型漏洞的方法。最后一个重点是开发解决模型中漏洞的方法，方法是在训练过程中将漏洞定义直接注入模型，并使用基于简化的注释来监督模型。这些推动力建立在行为测试、基于解释的交互和架构不可知论的目标之上，以支持大多数当前和未来的NLP模型和应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With recent advances in machine learning, models have achieved high accuracy on many challenging tasks in natural language processing (NLP) such as question answering, machine translation, and dialog agents, sometimes coming close to or beating human performance on these benchmarks. However, these NLP models often suffer from brittleness in many different ways: they latch onto erroneous artifacts, do not support natural variations in language, are not robust to adversarial attacks, and only work on a few domains. Existing pipelines for developing NLP models lack support for useful insights, and identifying bugs requires considerable effort from experts both in machine learning and the domain. This CAREER project develops several techniques to support this need for more robust training and evaluation pipelines for NLP, providing easy-to-use, scalable, and accurate mechanisms for identifying, understanding, and addressing NLP models' vulnerabilities. The developed methods will support diverse application areas such as conversational agents, sentiment classifiers, and abuse/hate speech detection. Further, the team engages with the developers of NLP models in academia and industry to develop a data science curriculum for K-12 education, particularly for students from underrepresented communities.Based on the notion of vulnerability as unexpected behavior on certain input transformations, the team will contribute across the following three thrusts. The first thrust identifies vulnerabilities by testing user-defined behaviors and searching over many possible vulnerabilities. In the second thrust, the investigators develop methods to understand the model's vulnerabilities by tracing the causes of errors to individual training data points and data artifacts. The last thrust will develop approaches to address vulnerabilities in models by directly injecting the vulnerability definitions into the model during training and using explanation-based annotations to supervise the models. These thrusts build upon the goals of behavioral testing, explanation-based interactions, and architecture agnosticism to support most current and future NLP models and applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.