5,449,913 AI resources indexedWorking toward the world’s largest AI resource index.
Publications about AI methods and applications.
Research paper by Kanak Gautam and collaborators. arXiv record dated 2026-03-31. Research area: Artificial intelligence.
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
Research paper by SymbiosisK (Katia) & GPT‑5 (Jippi), published 2026 in Zenodo (CERN European Organization for Nuclear Research). Research area: Psychiatry, Mental Health, Neuroscience.
Research paper by Xiaoyang Hou and collaborators, published 2026 in IEEE Transactions on Dependable and Secure Computing. Research area: Cryptography and Data Security.
Research paper by Wonwoo Choi and collaborators, published 2026 in arXiv (Cornell University). Research area: Adversarial Robustness in Machine Learning.
Medical image analysis is crucial in modern radiological diagnostics, especially given the exponential growth in medical imaging data. The demand for automated report generation systems has become increasingly urgent. While prior research has mainly focused on using machine learning and multimodal language models for 2D medical images, the generation of reports for 3D medical images has been less explored due to data scarcity and computational complexities. This paper introduces 3D-CT-GPT, a Visual Question Answering (VQA)-based medical visual language model specifically designed for generating radiology reports from 3D CT scans, particularly
Research paper by Chunyi Sun and collaborators. Published 2023-10-19. Topics: large language models (LLMs), instruction-driven 3D modeling, task dispatch agent, conceptualization agent, modeling agent.
Research paper by Wenxuan Zhu and collaborators. Published 2025-03-22. Topics: Multimodal Large Language Models, MLLMs, 4D object understanding, 4D-Bench, 4D object Question Answering.
Research paper by Chaoyou Fu and collaborators. Published 2023-12-19. Topics: Multi-modal Large Language Models, MLLMs, GPT-4V, Gemini, visual understanding.
The rapid advancement in Large Language Models has been met with significant challenges in their training processes, primarily due to their considerable computational and memory demands. This research examines parallelization techniques developed to address these challenges, enabling the efficient and scalable training of Large Language Models. A comprehensive analysis of both data and model parallelism strategies, including Fully Sharded Data Parallelism and Distributed Data-Parallel frameworks, is provided to assess methods that facilitate efficient model training. Furthermore, the architectural complexities and training methodologies of th
This article aims to analyze the translation of Jane Austen's novel Pride and Prejudice from English to Turkish using ChatGPT-4 and ChatGPT-4 Turbo models, evaluated through the lenses of cultural, semantic, and translation theories. The study investigates how ChatGPT-4 handles the transfer of meaning and cultural elements into the target language, while assessing the machine’s literary translation capabilities. Additionally, the article explores whether any meaning loss occurs during the translation process and if the target language used is appropriate for the audience. The article also compares ChatGPT’s translation with Hamdi Koç’s versio
The increasing adoption of artificial intelligence in journalism has created demand for language models that can support editorial workflows while complying with legal, ethical, and organizational requirements. In particular, concerns regarding copyrighted training data, transparency, and operational control have motivated interest in domain-specific small language models (SLMs) that can be trained entirely on licensed content. This study investigates the extent to which a custom domain-specific SLM is suitable for title generation in a journalistic environment when compared with the general-purpose large language model GPT-4o. The project wa
Try a broader search or clear some filters.
Platform labels describe intended use. Read each resource's author credits, requirements and available access methods.