Engineering
DistilBERT: A Distilled Version of BERT
DistilBERT was introduced as a smaller, faster, and distilled version of BERT. It maintains 97% of BERT's language understanding capabilities while being 40% small and 60% faster.
Community
Harnessing Embedding Models for AI-Powered Search
Building state-of-the-art embedding models for high-quality RAG systems needs careful attention to pretraining, fine-tuning, and scalability. Zilliz Cloud and Milvus help manage embeddings at scale and create more intelligent and responsive neural search systems.
Community
What is a Knowledge Graph (KG)?
A knowledge graph is a data structure representing information as a network of entities and their relationships.
Paper Reading
RoBERTa: An Optimized Method for Pretraining Self-supervised NLP Systems
RoBERTa (A Robustly Optimized BERT Pretraining Approach) is an improved version of BERT designed to address its limitations.
Community
Search Still Matters: Enhancing Information Retrieval with Generative AI and Vector Databases
Despite advances in LLMs like ChatGPT, search still matters. Combining GenAI with search and vector databases enhances search accuracy and experience.
Community
What is Information Retrieval?
Information retrieval (IR) is the process of efficiently retrieving relevant information from large collections of unstructured or semi-structured data.
Community
A Beginner's Guide to Understanding Vision Transformers (ViT)
Vision Transformers (ViTs) are neural network models that use transformers to perform computer vision tasks like object detection and image classification.
Community
Improving Information Retrieval and RAG with Hypothetical Document Embeddings (HyDE)
HyDE (Hypothetical Document Embeddings) is a retrieval method that uses "fake" documents to improve the answers of LLM and RAG.
Community
Enhancing Your RAG with Knowledge Graphs Using KnowHow
Knowledge Graphs (KGs) store and link data based on their relationships. KG-enhanced RAG can significantly improve retrieval capabilities and answer quality.