Recent advances in natural language processing (NLP) have produced general models that can perform complex tasks such as summarizing long passages and translating across languages. Here, we introduce a method to extract adjective similarities from language models as done with survey-based ratings in traditional psycholexical studies but using millions of times more text in a natural setting. The correlational structure produced through this method is highly similar to that of self- and other-ratings of 435 terms reported by Saucier and Goldberg (1996a). The first three unrotated factors produced using NLP are congruent with those in survey data, with coefficients of 0.89, 0.79, and 0.79. This structure is robust to many modeling decisions: adjective set, including those with 1,710 terms (Goldberg, 1982) and 18,000 terms (Allport & Odbert, 1936); the query used to extract correlations; and language model. Notably, Neuroticism and Openness are only weakly and inconsistently recovered. This is a new source of signal that is closer to the original (semantic) vision of the Lexical Hypothesis. The method can be applied where surveys cannot: in dozens of languages simultaneously, with tens of thousands of items, on historical text, and at extremely large scale for little cost. The code is made public to facilitate reproduction and fast iteration in new directions of research.
HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction
Department of Computer Science and Information Technology（计算机科学与信息技术系）；Institude for Advanced Studies in Basic Sciences (IASBS)（基础科学高级研究 institute (IASBS)）
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts
Information Sciences Institute, University of Southern California（南加州大学信息科学学院）；Iovine and Young Academy, University of Southern California（南加州大学约万与杨学院）；In Vivo Group
Lexical Access Model for Italian -- Modeling human speech processing: identification of words in running speech toward lexical access based on the detection of landmarks and other acoustic cues to features
Massachusetts Institute of Technology (MIT)（麻省理工学院）；Sapienza University of Rome（罗马大学）；Radcliffe Institute for Advanced Study, Harvard University（哈佛大学拉德克利夫高级研究所）
为了模拟听者在推导出说话者想要表达的词语时所激发的过程，需要对词汇项如何存储在记忆中设定一个假设。这项工作的目的是开发一个系统，模仿人类识别运行语音中的单词，并以这种方式，提供一个框架，以更好地理解人类的语音处理。我们建立了一个语音识别器为意大利语的基础上史蒂文斯的词汇访问模型的原则，其中单词存储为层次安排的区别性特征（史蒂文斯，K。n.（2002年）的报告。"Toward a model for lexical access based on acoustic landmarks and distinct features，" J. Acoust. Soc. Am.，111（4）：1872 - 1891）。在过去的几十年里，麻省理工学院（MIT）的语音通信组基于这种方法开发了一个英语语音识别系统。意大利语将是英语之外的第一种语言;扩展到另一种语言提供了一个机会来测试假设，即单词在记忆中表示为一组分层排列的独特特征，并揭示哪些潜在机制可能具有语言独立性。本文还介绍了一个新的词汇访问语料库，LaMIT数据库，创建和标记专门为这项工作，将免费提供给语音研究社区。未来的发展将测试这一假设，即特定的声学不连续性-称为地标-作为特征的线索，是语言独立的，而其他线索可能是语言依赖的，这对理解人类大脑如何识别语音具有重要意义。
Tokens, the oft-overlooked appetizer: Large language models, the distributional hypothesis, and meaning
Computational Story Lab（计算故事实验室）；Vermont Complex Systems Institute（佛蒙特复杂系统研究所）；University of Vermont（佛蒙特大学）；Department of Computer Science（计算机科学系）；St. Michael’s College（圣米歇尔学院）；Communication Media and Marginalization Lab（传播媒体与边缘化实验室）；Northeastern University（东北大学）；Department of Mathematics & Statistics（数学与统计学系）
Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models
Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos（莫雷洛斯州自治大学科学研究中心）
