CAREER: Robust, Fair, and Culturally Aware Commonsense Reasoning in Natural Language

人工智能的最新进展导致了大型语言模型(LLM)的激增。LLM是可用于通过书面语言与人类用户交互的模型；例如，用户将英语指令或问题输入基于LLM的程序，LLM以流利的英语输出响应。凭借这些语言能力，LLMS正在开发中，用于既普遍存在的应用程序(例如，互联网搜索、客户支持、写作工具)和高风险应用程序(例如，心理健康保健、课堂教育、残疾人辅助技术)。尽管它们被越来越多地采用，但LLM的许多基本性质还没有被很好地理解，而且仍然存在一些紧迫的问题，即LLM何时以及是否可以被赋予如此重要的任务。例如，当被指示对日常情况做出简单预测时，比如做饭或乘坐车辆，LLMS可能会犯奇怪而令人惊讶的错误，表现出对基本常识判断和推理能力的失误。此外，低收入国家做出的这些预测可能反映出社会陈规定型观念和文化假设，在最好的情况下，这会限制技术对某些人群的有用性，在最坏的情况下，会造成积极的伤害。该项目旨在解决由于刻板印象和文化背景造成的不公平和偏见，提出一个自然语言中可废止的常识推理的通用框架，其中一个系统比较两个类似情况对给定推理的支持。这项拟议的工作旨在开发科学的方法来衡量和提高LLM的能力，以(1)正确地对日常情况进行推理，(2)以公平和公正的方式这样做，以及(3)使这些推理能力适应特定的文化背景。通过测量LLMS的这些基本能力，我们可以更好地理解和降低在高风险环境中应用这项技术的风险。项目的三个阶段集中在(1)稳健性，(2)社会公平，和(3)LLMS中推理的文化意识维度。该项目假设了一个基本任务公式，其中向LLM提供了一个情况描述(例如，“有人掉了一个玻璃杯”)，并且LLM必须评估一个可能的推断，或者从头开始生成一个推断(“玻璃破碎”)。在第一阶段，将开发自动处理情况描述的方法，以训练和评估LLM做出细微推断的能力，目的是学习区分哪些因素影响特定的推断，哪些因素不影响(例如，当试图预测掉落的玻璃是否会破碎时，玻璃的厚度很重要，但玻璃的颜色不重要。)在第二阶段，将开发方法来自动测试LLM是否做出社会公平的推断，例如通过名称替换测试，并在检测到拟议的输出不公平时进行干预。在第三阶段，来自美国和加纳的调查参与者将回答关于日常情况的多个阶段的问题；收集的数据将用于为案例研究制定评估问题，以了解LLM在这两种文化背景下的适应性。对于项目的每个阶段，最终的数据集、方法和科学发现将向公众公布。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Recent advances in artificial intelligence have led to the proliferation of Large Language Models (LLMs). LLMs are models that cane be used for interactions with human users through written language; for example, a user inputs an instruction or question in English to the LLM-based program, and the LLM outputs a response in fluent English. With these linguistic capabilities, LLMs are being developed for use in applications that are both ubiquitous (e.g., internet search, customer support, writing tools) and high-stakes (e.g., mental health care, classroom education, assistive technology for people with disabilities). Despite their growing adoption, many fundamental properties of LLMs aren’t yet well understood, and pressing questions remain about when and whether LLMs can be entrusted with such important tasks. For example, when instructed to make simple predictions about every-day situations, like cooking a meal or riding in a vehicle, LLMs can make strange and surprising errors, exhibiting concerning lapses in basic common sense judgment and reasoning abilities. Additionally, these predictions made by LLMs can reflect social stereotypes and cultural assumptions which, at best, limit the usefulness of the technology for certain populations and, at worst, cause active harm. This project seeks to address unfairness and bias due to stereotyping and cultural context by proposing a generalized framework for defeasible commonsense inference in natural language in which a system compares two similar situations with respect to their support for a given inference. The proposed work aims at developing scientific methods to measure and improve the abilities of LLMs to (1) reason correctly about every-day situations, (2) do so in a manner that is fair and unprejudiced, and (3) adapt these reasoning abilities across specific cultural contexts. By measuring these fundamental capabilities of LLMs, we can better understand and mitigate the risks of applying this technology in high-stakes settings.The three phases of the project focus on the (1) robustness, (2) social fairness, and (3) cultural awareness dimensions of reasoning in LLMs. The project assumes a basic task formulation in which a situation description is provided to an LLM (e.g., “Someone drops a glass”), and the LLM must either evaluate a possible inference, or generate an inference from scratch (“The glass breaks”). In phase 1, methods will be developed to automatically manipulate situation descriptions in order to train and evaluate an LLM’s ability to make nuanced inferences, with the goal of learning to distinguish which factors influence a particular inference and which ones do not (e.g., when trying to predict if a dropped glass is going to break, the thickness of the glass matters but the color of the glass does not.) In phase 2, methods will be developed to automatically test whether LLMs make socially fair inferences, for example via name substitution tests, and to intervene when a proposed output is detected as unfair. In phase 3, survey participants from the U.S. and Ghana will answer multiple stages of questions about every-day situations; the collected data will be used to develop evaluation questions for a case study on the adaptability of LLMs across these two cultural settings. For each phase of the project, the resulting datasets, methods, and scientific findings will be made available to the public.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.