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Found 20 papers, 8 papers with code
Exploring the landscape of large language models: Foundations, techniques, and challenges
In this review paper, we delve into the realm of Large Language Models (LLMs), covering their foundational principles, diverse applications, and nuanced training processes.
Multi-level biomedical NER through multi-granularity embeddings and enhanced labeling
These results illustrate the proficiency of our proposed model in performing biomedical Named Entity Recognition.
Model-agnostic explainable artificial intelligence for object detection in image data
The experimentations on various object detection datasets and models showed that BODEM can be effectively used to explain the behavior of object detectors and reveal their vulnerabilities.
ThoughtSource: A central hub for large language model reasoning data
Large language models (LLMs) such as GPT-4 have recently demonstrated impressive results across a wide range of tasks.
A global analysis of metrics used for measuring performance in natural language processing
Our results suggest that the large majority of natural language processing metrics currently used have properties that may result in an inadequate reflection of a models' performance.
Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain
In this article, we first give an introduction to artificial intelligence and its applications in biology and medicine in Section 1.
Improving the robustness and accuracy of biomedical language models through adversarial training
Experimental results showed that the biomedical NLP models are sensitive to adversarial samples; their performance dropped in average by 21 and 18. 9 absolute percent on character-level and word-level adversarial noise, respectively.
GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain
However, in-domain pretraining seems not to be sufficient; novel pretraining and few-shot learning strategies are required in the biomedical NLP domain.
Deep learning models are not robust against noise in clinical text
The proposed perturbation methods can be used in performance evaluation tests to assess how robustly clinical NLP models can operate on noisy data, in real-world settings.
Evaluating the Robustness of Neural Language Models to Input Perturbations
High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks.
Hybrid deep learning methods for phenotype prediction from clinical notes
We also showed that BiGRU layer with FastText embedding had better performance than BiLSTM layer to identify patient phenotypes.
Explaining Black-box Models for Biomedical Text Classification
Confident itemsets discover how biomedical concepts are related to class labels in the black-box's decision space.
Explaining black-box text classifiers for disease-treatment information extraction
In this paper, we introduce a post-hoc explanation method that utilizes confident itemsets to approximate the behavior of black-box classifiers for medical information extraction.
A critical analysis of metrics used for measuring progress in artificial intelligence
Our results suggest that the large majority of metrics currently used have properties that may result in an inadequate reflection of a models' performance.
Post-hoc explanation of black-box classifiers using confident itemsets
We introduce confident itemsets, a set of feature values that are highly correlated to a specific class label.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Text Summarization in the Biomedical Domain
The primary purpose of this chapter is to review the most significant research efforts made in the current decade toward new methods of biomedical text summarization.
Clustering of Deep Contextualized Representations for Summarization of Biomedical Texts
In recent years, summarizers that incorporate domain knowledge into the process of text summarization have outperformed generic methods, especially for summarization of biomedical texts.
Small-world networks for summarization of biomedical articles
We show that how a summarizer can discover meaningful concepts within a biomedical text document using the Helmholtz principle.
A centralized reinforcement learning method for multi-agent job scheduling in Grid
In this method, there are a learner agent and several scheduler agents that perform the task of learning and job scheduling with the use of a coordination strategy that maintains the communication cost at a limited level.
Different approaches for identifying important concepts in probabilistic biomedical text summarization
Moreover, the results suggest that using the meaningfulness measure and considering the correlations of concepts in the feature selection step lead to a significant increase in the performance of summarization.
