**KnowPrompt** is a prompt-tuning approach for relational understanding. It injects entity and relation knowledge into prompt construction with learnable virtual template words as well as answer words and synergistically optimize their representation with knowledge constraints. To be specific, TYPED MARKER is utilized around entities initialized with aggregated entity-type embeddings as learnable virtual template words to inject entity type knowledge. The average embeddings of each token are leveraged in relation labels as virtual answer words to inject relation knowledge. Since there exist implicit structural constraints among entities and relations, and virtual words should be consistent with the surrounding contexts, synergistic optimization is introduced to obtain optimized virtual templates and answer words. Concretely, a context-aware prompt calibration method is used with implicit structural constraints to inject structural knowledge implications among relational triples and associate prompt embeddings with each other.

**Aging Evolution**, or **Regularized Evolution**, is an evolutionary algorithm for [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). Whereas in tournament selection, the best architectures are kept, in aging evolution we associate each genotype with an age, and bias the tournament selection to choose
the younger genotypes. In the context of architecture search, aging evolution allows us to explore the search space more, instead of zooming in on good models too early, as non-aging evolution would.

Aging Evolution

Regularized Evolution for Image Classifier Architecture Search

KnowPrompt

KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

**GHM-C** is a loss function designed to balance the gradient flow for anchor classification. The GHM first performs statistics on the number of examples with similar attributes w.r.t their gradient density and then attaches a harmonizing parameter to the gradient of each example according to the density. The modification of gradient can be equivalently implemented by reformulating the loss function. Embedding the GHM into the classification loss is denoted as GHM-C loss. Since the gradient density is a statistical variable depending on the examples distribution in a mini-batch, GHM-C is a dynamic loss that can adapt to the change of data distribution in each batch as well as to the updating of the model.

Source	KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com