Papers by Bernal Jimenez Gutierrez
Document Classification for COVID-19 Literature (2020.findings-emnlp)
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| Challenge: | a 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 variety of fields. |
| Approach: | They analyze a LitCovid dataset to find out how classification models can help organize COVID-19 research papers. |
| Outcome: | The proposed model outperforms all baseline models on the LitCovid dataset . it also outperformed BioBERT and other models with micro-F1 and accuracy scores of 86% and 75% . |
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)
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| Challenge: | Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs. |
| Approach: | They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction. |
| Outcome: | The proposed model underperforms on two representative biomedical information extraction tasks. |
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors (2023.findings-acl)
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| Challenge: | Recent work has shown that fine-tuning large language models on large instruction-following datasets improves their performance on a wide range of NLP tasks, but they fail to outperform small LMs on relation extraction (RE), a fundamental information extraction task. |
| Approach: | They propose a framework that aligns RE with question answering (QA), a predominant task in instruction-tuning datasets. |
| Outcome: | The proposed framework outperforms small LLMs on relation extraction (RE), a fundamental information extraction task, by a large margin. |
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset (2020.acl-main)
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| Challenge: | Medical professionals often query over clinical notes to find information that can support their decision making. |
| Approach: | They propose to use expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering based on clinical notes. |
| Outcome: | The proposed system can answer clinical questions without using domain knowledge. |