Low-resource MobileBERT for emotion recognition in imbalanced text datasets mitigating challenges with limited resources. by Muhammad Hussain, Caikou Chen, Sami S Albouq, Khlood Shinan, Fatmah Alanazi, Muhammad Waseem Iqbal, M Usman Ashraf

“…Our approach shows its potential to deliver competitive performance under limited resource constraints, comparable to large language models.…”
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Discovering CRISPR-Cas system with self-processing pre-crRNA capability by foundation models by Wenhui Li, Xianyue Jiang, Wuke Wang, Liya Hou, Runze Cai, Yongqian Li, Qiuxi Gu, Qinchang Chen, Peixiang Ma, Jin Tang, Menghao Guo, Guohui Chuai, Xingxu Huang, Jun Zhang, Qi Liu

“…However, traditional Cas discovery methods relying on sequence similarity may miss distant homologs and aren’t suitable for functional recognition. With protein large language models (LLMs) evolving, there is potential for Cas system modeling without extensive training data. …”
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Documenting Geographically and Contextually Diverse Language Data Sources by Angelina McMillan-Major, Francesco De Toni, Zaid Alyafeai, Stella Biderman, Kimbo Chen, Gérard Dupont, Hady Elsahar, Chris Emezue, Alham Fikri Aji, Suzana Ilić, Nurulaqilla Khamis, Colin Leong, Maraim Masoud, Aitor Soroa, Pedro Ortiz Suarez, Daniel van Strien, Zeerak Talat, Yacine Jernite

“… Contemporary large-scale data collection efforts have prioritized the amount of data collected to improve large language models (LLM). This quantitative approach has resulted in concerns for the rights of data subjects represented in data collections. …”
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AI Methods for Antimicrobial Peptides: Progress and Challenges by Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, Cesar de laFuente‐Nunez

“…Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure‐guided AMP discovery and design. …”
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Exploring the Role of AI-Based Conversational Agents for Collaborative Learning of English as a Foreign Language by Ebru Pınar Elmacı Er, Armin Weinberger

“…This paper investigates the evolving role of chat agents based on Artificial Intelligence (AI) and Large Language Models (LLM), notably ChatGPT, in the landscape of CL specific to EFL. …”
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On the role of knowledge graphs in AI-based scientific discovery by Mathieu d’Aquin

“…Research and the scientific activity are widely seen as an area where the current trends in AI, namely the development of deep learning models (including large language models), are having an increasing impact. …”
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The Machine as an Autonomous Explanatory Agent by Dilek Yargan

“…The prowess of Large Language Models (LLMs) serves as evidence: they enable seamless natural language communication and widespread use across various fields by swiftly processing unstructured data and handling diverse datasets with agility. …”
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CuTCP: Custom Text Generation-based Class-aware Prompt Tuning for visual-language models by Min Huang, Chen Yang, Xiaoyan Yu

“…CuTCP leverages large language models to generate descriptive, category-specific prompts, embedding richer semantic information that enhances the model’s ability to differentiate between known and unseen categories. …”
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CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking by Hansi Hettiarachchi, Amna Dridi, Mohamed Medhat Gaber, Pouyan Parsafard, Nicoleta Bocaneala, Katja Breitenfelder, Gonçal Costa, Maria Hedblom, Mihaela Juganaru-Mathieu, Thamer Mecharnia, Sumee Park, He Tan, Abdel-Rahman H. Tawil, Edlira Vakaj

“…It enables applying recent trends, such as deep neural networks and large language models, to ACC.…”
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Incremental accumulation of linguistic context in artificial and biological neural networks by Refael Tikochinski, Ariel Goldstein, Yoav Meiri, Uri Hasson, Roi Reichart

“…Abstract Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. …”
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Unveiling the power of language models in chemical research question answering by Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Zirui Song, Xin Gao, Xiangliang Zhang

“…Experiments show that Large Language Models (LLMs) still have significant room for improvement in the field of chemistry. …”
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Clinical entity augmented retrieval for clinical information extraction by Ivan Lopez, Akshay Swaminathan, Karthik Vedula, Sanjana Narayanan, Fateme Nateghi Haredasht, Stephen P. Ma, April S. Liang, Steven Tate, Manoj Maddali, Robert Joseph Gallo, Nigam H. Shah, Jonathan H. Chen

“…Abstract Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. …”
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AI language model rivals expert ethicist in perceived moral expertise by Danica Dillion, Debanjan Mondal, Niket Tandon, Kurt Gray

“…Recent work suggests that large language models (LLMs) perform well on tasks designed to assess moral alignment, reflecting moral judgments with relatively high accuracy. …”
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A latent diffusion approach to visual attribution in medical imaging by Ammar Adeel Siddiqui, Santosh Tirunagari, Tehseen Zia, David Windridge

“…We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models, in order to generate normal counterparts of abnormal images. …”
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Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping by Dino Dobrinić, Mario Miler, Damir Medak

“…., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. …”
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The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models. by Ahmed Daoud, Asa Ben-Hur

“…Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources. …”
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Language writ large: LLMs, ChatGPT, meaning, and understanding by Stevan Harnad

“…Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how Large Language Models (LLMs) such as ChatGPT work (their vast text databases, statistics, vector representations, and huge number of parameters, next-word training, etc.). …”
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Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis by Jun-Min Lee, Tae-Bin Ha

“…We expect to see extended research combining Large Language Models with a new perspective of viewing text as an continuous space.…”
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Understanding ChatGPT: Impact Analysis and Path Forward for Teaching Computer Science and Engineering by P. Banerjee, Anurag K. Srivastava, Donald A. Adjeroh, Ramana Reddy, Nima Karimian

“…Large Language Models (LLMs) like ChatGPT have become the most popular regenerative AI applications, used for obtaining responses for queries in different domains. …”
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METAGRAPH THEORY AS A BASIS FOR MODELING RELEVANT MEDIA DISCOURSE by Yuriy E. Gapanyuk

“…Despite significant advances in the field of neural network text processing, the task of modeling text and media discourse remains relevant. Large language models cannot be considered as a reliable discourse model, due to the fact that they are susceptible to hallucinations, which are features of model training and are difficult to diagnose and eliminate in practice. …”
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