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.
This article provides a comparative analysis of popular embedding libraries for generative AI, evaluating their strengths, limitations, and suitability for different use cases.
A Github Gist containing a Python script for text classification using the TxTail API
In this article, we will explore various aspects of BERT, including the landscape at the time of its creation, a detailed breakdown of the model architecture, and writing a task-agnostic fine-tuning pipeline, which we demonstrated using sentiment analysis. Despite being one of the earliest LLMs, BERT has remained relevant even today, and continues to find applications in both research and industry.
This article explains how to use the Sentence Transformers library to finetune and train embedding models for a variety of applications, such as retrieval augmented generation, semantic search, and semantic textual similarity. It covers the training components, dataset format, loss function, training arguments, evaluators, and trainer.
Researchers from NYU Tandon School of Engineering investigated whether modern natural language processing systems could solve the daily Connections puzzles from The New York Times. The results showed that while all the AI systems could solve some of the puzzles, they struggled overall.
This article provides a beginner-friendly introduction to Large Language Models (LLMs) and explains the key concepts in a clear and organized way.
ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
- Embeddings transform words and sentences into sequences of numbers for computers to understand language.
- This technology powers tools like Siri, Alexa, Google Translate, and generative AI systems like ChatGPT, Bard, and DALL-E.
- In the early days, embeddings were crafted by hand, which was time-consuming and couldn't adapt to language nuances easily.
- The 3D hand-crafted embedding app provides an interactive experience to understand this concept.
- The star visualization method offers an intuitive way to understand word embeddings.
- Machine learning models like Word2Vec and GloVe revolutionized the generation of word embeddings from large text datasets.
- Universal Sentence Encoder (USE) extends the concept of word embeddings to entire sentences.
- TensorFlow Projector is an advanced tool to interactively explore high-dimensional data like word and sentence embeddings.
- This technology powers tools like Siri, Alexa, Google Translate, and generative AI systems like ChatGPT, Bard, and DALL-E.
- In the early days, embeddings were crafted by hand, which was time-consuming and couldn't adapt to language nuances easily.
- The 3D hand-crafted embedding app provides an interactive experience to understand this concept.
- The star visualization method offers an intuitive way to understand word embeddings.
- Machine learning models like Word2Vec and GloVe revolutionized the generation of word embeddings from large text datasets.
- Universal Sentence Encoder (USE) extends the concept of word embeddings to entire sentences.
- TensorFlow Projector is an advanced tool to interactively explore high-dimensional data like word and sentence embeddings.
