Distillation of Transformers: Techniques for Model Compression
Distillation of Transformers reduces model size while preserving performance through teacher-student training strategies.
What is Masked Language Modeling? Foundations and Applications
What is Masked Language Modeling? Learn how hiding tokens trains bidirectional context in Transformer models.
Ethical Data Sourcing for Large Language Models: A Technical Perspective
Ethical Data Sourcing for LLMs ensures accuracy, reduces bias, and respects privacy in large-scale corpus creation.
Deep Dive into Self-Attention: Core Principle of Transformers
Deep Dive into Self-Attention highlights how tokens attend to all positions to capture context in Transformer layers.
Transfer Learning in NLP: How Transformers Boost Downstream Tasks
Transfer Learning in NLP accelerates model development by fine-tuning large pre-trained Transformers for specific tasks.
What is RNN vs Transformer? Key Differences and Use Cases
What is RNN vs Transformer? Compare sequential processing in RNNs to the parallel attention-based approach of Transformers.
Multilingual Transformers: Breaking Language Barriers
Multilingual Transformers unify cross-lingual tasks, leveraging shared embeddings for global language understanding.
Evaluating LLM Performance: Metrics, Benchmarks, and Limitations
Evaluating LLM Performance reviews perplexity, BLEU, and other metrics to gauge model accuracy and efficiency.
Advances in Tokenization Strategies for LLMs
Tokenization Strategies influence LLM performance, segmenting text and improving representation for neural encoders.
Attention is All You Need: Revisiting the Seminal Transformer Paper
Attention is All You Need revisits the core paper that introduced the Transformer, shaping modern NLP.
