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The deployment of Large Language Models (LLMs) in real-world applications presents both opportunities and challenges, particularly in multilingual and code-mixed communication settings. This research evaluates the performance of seven…

Large language models (LLMs) offer a powerful opportunity to simulate the results of social science experiments. In this work, we demonstrate that finetuning LLMs directly on individual-level responses from past experiments meaningfully…

机器学习 · 计算机科学 2025-11-07 Akaash Kolluri , Shengguang Wu , Joon Sung Park , Michael S. Bernstein

Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) have shown promise in replicating…

计算与语言 · 计算机科学 2025-06-23 Ziyan Cui , Ning Li , Huaikang Zhou

Large language models (LLMs) are increasingly used both to make decisions in domains such as health, education and law, and to simulate human behavior. Yet how closely LLMs mirror actual human decision-making remains poorly understood. This…

人工智能 · 计算机科学 2025-11-26 Andrea Cera Palatsi , Samuel Martin-Gutierrez , Ana S. Cardenal , Max Pellert

As Large Language Models (LLMs) become widely used to model and simulate human behavior, understanding their biases becomes critical. We developed an experimental framework using Big Five personality surveys and uncovered a previously…

Large Language Models (LLMs) are increasingly deployed for open-domain question answering, yet their alignment with human perspectives on temporally recent information remains underexplored. We introduce RECOM (Reddit Evaluation for…

计算与语言 · 计算机科学 2026-02-13 Pushwitha Krishnappa , Amit Das , Vinija Jain , Tathagata Mukherjee , Aman Chadha

Large Language Models (LLMs) represent a major step toward artificial general intelligence, significantly advancing our ability to interact with technology. While LLMs perform well on Natural Language Processing tasks -- such as…

计算与语言 · 计算机科学 2025-05-15 Brandon Smith , Mohamed Reda Bouadjenek , Tahsin Alamgir Kheya , Phillip Dawson , Sunil Aryal

Understanding the similarity of the numerous released large language models (LLMs) has many uses, e.g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well. In this…

机器学习 · 计算机科学 2023-12-06 Max Klabunde , Mehdi Ben Amor , Michael Granitzer , Florian Lemmerich

Recent research has focused on examining Large Language Models' (LLMs) characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administration of personality tests…

计算与语言 · 计算机科学 2024-10-07 Jen-tse Huang , Wenxiang Jiao , Man Ho Lam , Eric John Li , Wenxuan Wang , Michael R. Lyu

Large Language Models (LLMs) are increasingly being used in educational and learning applications. Research has demonstrated that controlling for style, to fit the needs of the learner, fosters increased understanding, promotes inclusion,…

计算与语言 · 计算机科学 2024-06-19 Ankit Aich , Tingting Liu , Salvatore Giorgi , Kelsey Isman , Lyle Ungar , Brenda Curtis

Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted LLMs for text classification tasks, which are difficult to…

计算与语言 · 计算机科学 2024-11-05 Marcello Carammia , Stefano Maria Iacus , Giuseppe Porro

The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of…

计算与语言 · 计算机科学 2024-05-14 Nikolay B Petrov , Gregory Serapio-García , Jason Rentfrow

In an era where large language models (LLMs) are increasingly integrated into a wide range of everyday applications, research into these models' behavior has surged. However, due to the novelty of the field, clear methodological guidelines…

计算与语言 · 计算机科学 2024-10-01 Laurène Vaugrante , Mathias Niepert , Thilo Hagendorff

We systematically evaluate the reproducibility of data analysis conducted by Large Language Models (LLMs). We evaluate two prompting strategies, six models, and four temperature settings, with ten independent executions per configuration,…

应用统计 · 统计学 2026-02-17 Jiaxin Cui , Rohan Alexander

Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey…

This paper explores the potential of large language models (LLMs) as reliable analytical tools in linguistic research, focusing on the emergence of affective meanings in temporal expressions involving manner-of-motion verbs. While LLMs like…

计算与语言 · 计算机科学 2025-07-15 Rosa Illan Castillo , Javier Valenzuela

Large language models (LLMs) exhibit expert-level performance in tasks across a wide range of different domains. Ethical issues raised by LLMs and the need to align future versions makes it important to know how state of the art models…

Large language models (LLMs) achieve impressive results in terms of fluency in text generation, yet the nature of their linguistic knowledge - in particular the human-likeness of their internal lexicon - remains uncertain. This study…

计算与语言 · 计算机科学 2026-03-20 Maria Andueza Rodriguez , Marie Candito , Richard Huyghe

Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reproduce patterns of susceptibility to misinformation remains unclear. We test whether LLM-simulated…

社会与信息网络 · 计算机科学 2026-04-13 Eun Cheol Choi , Lindsay E. Young , Emilio Ferrara

The success of Large Language Models (LLMs) in multicultural environments hinges on their ability to understand users' diverse cultural backgrounds. We measure this capability by having an LLM simulate human profiles representing various…

计算与语言 · 计算机科学 2024-08-14 Louis Kwok , Michal Bravansky , Lewis D. Griffin
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