GPT vs BERT: Comparing Auto-Regressive and Auto-Encoding Approaches
GPT vs BERT unpacks the differences in forward-only generation versus masked token inference for NLP tasks.
Scaling Laws in Language Models: Understanding Performance vs Model Size
Scaling Laws in Language Models highlight how bigger architectures often yield better results but raise computational costs.
What is Beam Search? Decoding Strategies in Transformer Models
What is Beam Search? Discover how controlled breadth-first search refines text generation in Transformers.
The Math Behind Transformers: Linear Algebra and Matrix Multiplications
The Math Behind Transformers includes matrix multiplications, projections, and attention weight calculations for text processing.
What is RAG for Chatbots? Crafting Interactive Retrieval-Augmented Systems
What is RAG for Chatbots? Merge knowledge bases and generative models to supply contextually accurate answers.
Large Language Models for Code Generation: Prospects and Limitations
Large Language Models for Code can produce executable snippets, but require proper validation and debugging.
Transformer Model Interpretability: Visualizing Attention Heads
Transformer Model Interpretability enables comprehension of attention patterns and layer contributions.
What is Causal Language Modeling? Understanding One-Way Context
What is Causal Language Modeling? Delve into one-directional text generation and predictive token modeling.
Fine-Tuning vs Prompt Tuning: Which Method Suits Your NLP Project?
Fine-Tuning vs Prompt Tuning explores contrasting approaches to adapt LLMs for specialized tasks.
Transformer vs CNN: Comparative Analysis for Sequence Processing
Transformer vs CNN examines attention-driven architectures versus convolutional methods in NLP.
