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Found 33 papers, 20 papers with code
Dataset Debt in Biomedical Language Modeling
Large-scale language modeling and natural language prompting have demonstrated exciting capabilities for few and zero shot learning in NLP.
An automatically discovered chain-of-thought prompt generalizes to novel models and datasets
Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs).
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.
Applying unsupervised keyphrase methods on concepts extracted from discharge sheets
Considering that most of the clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify n-gram range, we have proposed a shortcut method to preserve the structure of the expression based on TF-IDF.
ThoughtSource: A central hub for large language model reasoning data
Large language models (LLMs) such as GPT-3 and ChatGPT have recently demonstrated impressive results across a wide range of tasks.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance.
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.
Mapping global dynamics of benchmark creation and saturation in artificial intelligence
Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI).
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.
Benchmark datasets driving artificial intelligence development fail to capture the needs of medical professionals
Furthermore, there is a lack of systematized meta-information that allows clinical AI researchers to quickly determine accessibility, scope, content and other characteristics of datasets and benchmark datasets relevant to the clinical domain.
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.
A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks
Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies.
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BACKGROUND: In this study, we investigated the efficacy of current state-of-the-art neural sentence embedding models for semantic similarity estimation of sentences from biomedical literature.
Ranked #1 on
Sentence Embeddings For Biomedical Texts
on BIOSSES
SAFRAN: An interpretable, rule-based link prediction method outperforming embedding models
SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10.
Ranked #4 on
Link Prediction
on FB15k-237
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.
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.
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.
A Study into patient similarity through representation learning from medical records
To address the limitations of previous approaches in handling complex parts of EMR data, an unsupervised method is proposed for building a patient representation, which integrates unstructured data with structured data extracted from patients' EMRs.
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.
Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmark FB15K-237 and the large-scale biomedical benchmark OpenBioLink.
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.
Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
Recently, link prediction algorithms based on neural embeddings have gained tremendous popularity in the Semantic Web community, and are extensively used for knowledge graph completion.
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.
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
OpenBioLink: A benchmarking framework for large-scale biomedical link prediction
SUMMARY: Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks.
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.
Global and local evaluation of link prediction tasks with neural embeddings
We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities.
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge
In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge.
Applying deep learning techniques on medical corpora from the World Wide Web: a prototypical system and evaluation
We found that the ranking and retrieved results generated by word2vec were not of sufficient quality for automatic population of knowledge bases and ontologies, but could serve as a starting point for further manual curation.
