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Found 7 papers, 7 papers with code
Description Boosting for Zero-Shot Entity and Relation Classification
Even a minor modification of descriptions can lead to a change in the decision boundary between entity (or relation) classes.
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction
Additionally, we have designed our framework to support the industry with readily available APIs for production under the standard SpaCy NLP pipeline.
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences.
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption.
Ensembling Graph Predictions for AMR Parsing
In many machine learning tasks, models are trained to predict structure data such as graphs.
Ranked #2 on
AMR Parsing
on LDC2020T02
(using extra training data)
Neural Unification for Logic Reasoning over Natural Language
The RuleTaker approach of (Clark et al., 2020) achieves appealing results both in terms of accuracy and in the ability to generalize, showing that when the model is trained with deep enough queries (at least 3 inference steps), the transformers are able to correctly answer the majority of queries (97. 6%) that require up to 5 inference steps.
