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Found 5 papers, 5 papers with code
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting.
Biomedical Language Models are Robust to Sub-optimal Tokenization
As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise.
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting.
Ranked #1 on
Relation Extraction
on SemEval-2010 Task 8
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again
In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i. e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction.
Document Classification for COVID-19 Literature
The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields.
