An explanation of the differences between encoder- and decoder-style large language model (LLM) architectures, including their roles in tasks such as classification, text generation, and translation.
Snowflake recently announced the launch of Arctic Embed L 2.0 and Arctic Embed M 2.0, two small and powerful embedding models tailored for multilingual search and retrieval. The models are available in medium and large variants, with the medium model incorporating 305 million parameters and the large variant with 568 million parameters. Both models support context lengths of up to 8,192 tokens. They demonstrate high-quality retrieval across multiple languages and excel in benchmarks like MTEB and CLEF.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
Researchers from Cornell University developed a technique called 'contextual document embeddings' to improve the performance of Retrieval-Augmented Generation (RAG) systems, enhancing the retrieval of relevant documents by making embedding models more context-aware.
Standard methods like bi-encoders often fail to account for context-specific details, leading to poor performance in application-specific datasets. Contextual document embeddings address this by enhancing the sensitivity of the embedding model to subtle differences in documents, particularly in specialized domains.
The researchers proposed two complementary methods to improve bi-encoders:
- Modifying the training process using contrastive learning to distinguish between similar documents.
- Modifying the bi-encoder architecture to incorporate corpus context during the embedding process.
These modifications allow the model to capture both the general context and specific details of documents, leading to better performance, especially in out-of-domain scenarios. The new technique has shown consistent improvements over standard bi-encoders and can be adapted for various applications beyond text-based models.
Standard methods like bi-encoders often fail to account for context-specific details, leading to poor performance in application-specific datasets. Contextual document embeddings address this by enhancing the sensitivity of the embedding model to subtle differences in documents, particularly in specialized domains.
The researchers proposed two complementary methods to improve bi-encoders:
- Modifying the training process using contrastive learning to distinguish between similar documents.
- Modifying the bi-encoder architecture to incorporate corpus context during the embedding process.
These modifications allow the model to capture both the general context and specific details of documents, leading to better performance, especially in out-of-domain scenarios. The new technique has shown consistent improvements over standard bi-encoders and can be adapted for various applications beyond text-based models.
Foundational concepts, practical implementation of semantic search, and the workflow of RAG, highlighting its advantages and versatile applications.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article provides a comparative analysis of popular embedding libraries for generative AI, evaluating their strengths, limitations, and suitability for different use cases.
This paper surveys different prompt engineering techniques used to improve the performance of large language models on various Natural Language Processing (NLP) tasks. It categorizes these techniques by NLP task, highlights their performance on different datasets, and discusses state-of-the-art methods for specific datasets. The survey covers 44 research papers exploring 39 prompting methods across 29 NLP tasks.
