Direct Nash Optimization Beats Bigger Models with Better Data | HackerNoonOffline contrastive training provides more valuable signals for model performance than traditional supervised fine-tuning methods.
Improving Text Embeddings with Large Language Models: Instructions for Training and Evaluation | HackerNoonSynthetic data generation can enhance training models for multilingual retrieval tasks significantly.Contrastive pre-training may not always be necessary based on task context.
Improving Text Embeddings with Large Language Models: Is Contrastive Pre-training Necessary? | HackerNoonWeakly-supervised contrastive pre-training is essential for effective text embedding models.
Enhancing Rhetorical Role Labeling with Training-Time Neighborhood Learning | HackerNoonDiscourse-aware contrastive loss significantly enhances embedding performance by better organizing sentence relationships.
Direct Nash Optimization Beats Bigger Models with Better Data | HackerNoonOffline contrastive training provides more valuable signals for model performance than traditional supervised fine-tuning methods.
Improving Text Embeddings with Large Language Models: Instructions for Training and Evaluation | HackerNoonSynthetic data generation can enhance training models for multilingual retrieval tasks significantly.Contrastive pre-training may not always be necessary based on task context.
Improving Text Embeddings with Large Language Models: Is Contrastive Pre-training Necessary? | HackerNoonWeakly-supervised contrastive pre-training is essential for effective text embedding models.
Enhancing Rhetorical Role Labeling with Training-Time Neighborhood Learning | HackerNoonDiscourse-aware contrastive loss significantly enhances embedding performance by better organizing sentence relationships.