Research paper by Yu Gu and collaborators. Published 2023-07-12. Topics: large language models, LLMs, GPT-4, biomedical knowledge curation, distillation.
Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon
Research paper by Hrishita Panjetha and collaborators, published 2026 in BMC Medical Informatics and Decision Making. Research area: Topic Modeling.
Hrishita Panjetha, Jitendra Singh Jadon, Dipti Theng, Sofia Singh
In this system report, we describe the models and methods we used for our participation in the PLABA2023 task on biomedical abstract simplification, part of the TAC 2023 tracks. The system outputs we submitted come from the following three categories: 1) domain fine-tuned T5-like models including Biomedical-T5 and Lay-SciFive; 2) fine-tuned BARTLarge model with controllable attributes (via tokens) BART-w-CTs; 3) ChatGPTprompting. We also present the work we carried out for this task on BioGPT finetuning. In the official automatic evaluation using SARI scores, BeeManc ranks 2nd among all teams and our model LaySciFive ranks 3rd among all 13 ev
Li, Zihao, Belkadi, Samuel, Micheletti, Nicolo, Han, Lifeng, Shardlow, Matthew, Nenadic, Goran
Research paper by Md Serajun Nabi and collaborators, published 2026 in IEEE International Conference on Signal and Image Processing Applications (ICSIPA). Research area: AI in cancer detection.
Md Serajun Nabi, Md Saiful Arefin, Mohammad Faizal Ahmad Fauzi