Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. In this study, we utilize GPT-3.5-Turbo and Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender, across over 750,000 prompts. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occ
Nghiem, Huy, Prindle, John, Zhao, Jieyu, Daumé, Hal
Research paper by Christopher Rae and collaborators. Published 2024-05-28. Topics: Deep Neural Networks (DNNs), pipeline parallelism, backward propagation, 2-stage backpropagation (2BP), throughput.
Christopher Rae, Joseph K. L. Lee, James Richings
Research paper by Sergey Pletenev and collaborators. Published 2025-09-10. Topics: Large Language Models, LLMs, text detoxification, Llama 3, Qwen.
Sergey Pletenev, Daniil Moskovskiy, Alexander Panchenko
[Depreciated and replaced by V3] The application-specific clean rebuild has not yet been published; its authoritative theoretical boundary is now the governing V3 branch: After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transpar
Smith, Maria
This paper presents a comprehensive comparative analysis of Large Language Models (LLMs) for generation of code documentation. Code documentation is an essential part of the software writing process. The paper evaluates models such as GPT-3.5, GPT-4, Bard, Llama2, and Starchat on various parameters like Accuracy, Completeness, Relevance, Understandability, Readability and Time Taken for different levels of code documentation. Our evaluation employs a checklist-based system to minimize subjectivity, providing a more objective assessment. We find that, barring Starchat, all LLMs consistently outperform the original documentation. Notably, close
Dvivedi, Shubhang Shekhar, Vijay, Vyshnav, Pujari, Sai Leela Rahul, Lodh, Shoumik, Kumar, Dhruv
Research paper by Schmidt Albert, published 2026 in Research Square. Research area: Topic Modeling.
Schmidt Albert
Research paper by AmirAhmad Amjadi and collaborators, published 2025 in Research Square. Research area: Topic Modeling.
AmirAhmad Amjadi, Shiva TaghipourEivazi, Bahman Arasteh, Hüseyin Kusetoğulları
Research paper by Chaima Ghouil and collaborators, published 2026 in International Wireless Communications and Mobile Computing Conference (IWCMC). Research area: Adversarial Robustness in Machine Learning.
Chaima Ghouil, Radhoine Aloui, Néjib Khalfaoui, Sofien Mhatli, Adel Sharar Aldalbahi, Ignacio Llamas‐Garro
Research paper by Marcin Pietroń and collaborators. arXiv record dated 2026-02-25. Research area: Language technology.
Marcin Pietroń, Filip Gampel, Jakub Gomułka, Andrzej Tomski, Rafał Olszowski
Research paper by Marcin Pietroń and collaborators. arXiv record dated 2025-07-24. Research area: Language technology.
Marcin Pietroń, Rafał Olszowski, Jakub Gomułka, Filip Gampel, Andrzej Tomski
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1 405B face limitations due to large parameter sizes and computational demands, often requiring cloud API use which raises privacy concerns, limits real-time applications on edge devices, and increases fine-tuning costs. Additionally, LLMs often underperform in specialized domains such as healthcare and law due to insufficient domain-specific knowledge, necessitating specialized models. Therefore, Small Langua
Wang, Fali, Zhang, Zhiwei, Zhang, Xianren, Wu, Zongyu, Mo, Tzuhao, Lu, Qiuhao, Wang, Wanjing, Li, Rui, Xu, Junjie, Tang, Xianfeng, He, Qi, Ma, Yao, Huang, Ming, Wang, Suhang
OBJECTIVES To develop a content-aware chatbot based on GPT-3.5-Turbo and GPT-4 with specialized knowledge on the German S2 Cone-Beam CT (CBCT) dental imaging guideline and to compare the performance against humans. METHODS The LlamaIndex software library was used to integrate the guideline context into the chatbots. Based on the CBCT S2 guideline, 40 questions were posed to content-aware chatbots and early career and senior practitioners with different levels of experience served as reference. The chatbots' performance was compared in terms of recommendation accuracy and explanation quality. Chi-square test and one-tailed Wilcoxon signed rank
Russe, Maximilian Frederik, Rau, Alexander, Ermer, Michael Andreas, Rothweiler, René, Wenger, Sina, Klöble, Klara, Schulze, Ralf Kurt Willy, Bamberg, Fabian, Schmelzeisen, Rainer, Reisert, Marco, Semper-Hogg, Wiebke
