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，用户用英语向基于LLM的程序输入指令或问题，LLM将用流利的英语输出响应。有了这些语言能力，法学硕士正在被开发用于无处不在的应用程序（例如，互联网搜索，客户支持，写作工具）和高风险的应用程序（例如，精神卫生保健，课堂教育，残疾人辅助技术）。尽管法学硕士被越来越多地采用，但法学硕士的许多基本特性还没有得到很好的理解，而且关于法学硕士何时以及是否可以承担如此重要的任务仍然存在紧迫的问题。例如，当被要求对日常情况做出简单的预测时，比如做饭或开车，法学硕士们可能会犯奇怪而令人惊讶的错误，表现出基本常识判断和推理能力的失误。此外，法学硕士做出的这些预测可能反映了社会刻板印象和文化假设，这在最好的情况下会限制该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。该项目旨在解决刻板印象和文化背景造成的不公平和偏见，提出了一个自然语言中可推翻的常识推理的广义框架，其中系统比较两种类似情况对给定推理的支持程度。拟议的工作旨在发展科学的方法来衡量和提高法学硕士的能力，以：(1)正确地推理日常情况，(2)以公平和无偏见的方式这样做，(3)在特定的文化背景下适应这些推理能力。通过测量llm的这些基本能力，我们可以更好地理解和减轻在高风险环境中应用这项技术的风险。该项目的三个阶段侧重于(1)法学硕士推理的鲁棒性，(2)社会公平性和(3)文化意识维度。项目假设一个基本的任务公式，在这个公式中，向LLM提供了一个情况描述（例如，“有人掉了一个玻璃杯”），LLM必须评估一个可能的推断，或者从头生成一个推断（“玻璃杯碎了”）。在第一阶段，将开发自动操纵情况描述的方法，以训练和评估法学硕士做出细微推理的能力，目标是学习区分哪些因素会影响特定的推理，哪些因素不会（例如，当试图预测一个掉落的玻璃杯是否会破碎时，玻璃的厚度很重要，但玻璃的颜色无关紧要）。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，并在检测到提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有关日常情况的多个阶段的问题；收集到的数据将被用来为法学硕士在这两种文化背景下的适应性的案例研究开发评估问题。在项目的每个阶段，最终的数据集、方法和科学发现都将向公众开放。该奖项反映了美国国家科学基金会的法定使命，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。

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.