ODSC - Open Data Science

OCTOBER 27, 2023

Companies in sectors like healthcare, finance, legal, retail, and manufacturing frequently handle large numbers of documents as part of their day-to-day operations. These documents often contain vital information that drives timely decision-making, essential for ensuring top-tier customer satisfaction, and reduced customer churn.

IDP 98

IDP LLM Large Language Models Data Science 98

Unite.AI

JANUARY 11, 2024

Text embeddings are vector representations of words, sentences, paragraphs or documents that capture their semantic meaning. Synthetic Data Generation: Prompt the LLM with the designed prompts to generate hundreds of thousands of (query, document) pairs covering a wide variety of semantic tasks across 93 languages.

Large Language Models 147

Large Language Models Prompt Engineer Prompt Engineering BERT 147

AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning Anthropic’s Claude 3 Haiku has demonstrated superior performance compared to few-shot prompt engineering on base Anthropic’s Claude 3 Haiku, Anthropic’s Claude 3 Sonnet, and Anthropic’s Claude 3.5 Sonnet across various tasks. This decision should be based either on the provided context or your general knowledge and memory.

LLM 91

LLM Prompt Engineer Prompt Engineering Generative AI 91

AWS Machine Learning Blog

JUNE 25, 2024

Manually analyzing and categorizing large volumes of unstructured data, such as reviews, comments, and emails, is a time-consuming process prone to inconsistencies and subjectivity. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.

Automation 119

Automation Prompt Engineering Prompt Engineer Categorization 119

AWS Machine Learning Blog

MAY 7, 2024

Tasks such as routing support tickets, recognizing customers intents from a chatbot conversation session, extracting key entities from contracts, invoices, and other type of documents, as well as analyzing customer feedback are examples of long-standing needs. We also examine the uplift from fine-tuning an LLM for a specific extractive task.

Prompt Engineering 115

Prompt Engineering Prompt Engineer Large Language Models LLM 115

AWS Machine Learning Blog

SEPTEMBER 14, 2023

Document categorization or classification has significant benefits across business domains – Improved search and retrieval – By categorizing documents into relevant topics or categories, it makes it much easier for users to search and retrieve the documents they need. politics, sports) that a document belongs to.

Categorization 115

Categorization Machine Learning Data Scientist Natural Language Processing 115

AWS Machine Learning Blog

MARCH 13, 2024

Furthermore, the knowledge base includes the referenced policy documents used by the evaluation, providing moderators with additional context. This enables you to manage the policy document flexibly, allowing the workflow to retrieve only the relevant policy segments for each input message.

Natural Language Processing 127

Natural Language Processing LLM Prompt Engineer Prompt Engineering 127

AWS Machine Learning Blog

NOVEMBER 7, 2024

Some components are categorized in groups based on the type of functionality they exhibit. Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. Having a centralized prompt catalog is essential for storing, versioning, tracking, and sharing prompts.

Generative AI 106

Generative AI Machine Learning AI AI 106

AWS Machine Learning Blog

AUGUST 22, 2024

It streamlines document review for anyone needing to identify medical information within records, including bodily injury claims adjusters and managers, nurse reviewers and physicians, administrative staff, and legal professionals. These medical records are mostly unstructured documents, often containing multiple dates of service.

Generative AI 104

Generative AI Auto-complete Software Development Automation 104

Artificial Corner

JULY 30, 2023

Large language Models also intersect with Generative Ai, it can perform a variety of Natural Language Processing tasks, including generating and classifying text, question answering, and translating text from one language to another language, and Document summarization. What are large language models used for?

Large Language Models 98

Large Language Models LLM Natural Language Processing Deep Learning 98

AWS Machine Learning Blog

FEBRUARY 12, 2025

Curated judge models : Amazon Bedrock provides pre-selected, high-quality evaluation models with optimized prompt engineering for accurate assessments. Users dont need to bring external judge models, because the Amazon Bedrock team maintains and updates a selection of judge models and associated evaluation judge prompts.

LLM 88

LLM Generative AI Automation Machine Learning 88

Unite.AI

JANUARY 19, 2024

Taxonomy of Hallucination Mitigation Techniques Researchers have introduced diverse techniques to combat hallucinations in LLMs, which can be categorized into: 1. Prompt Engineering This involves carefully crafting prompts to provide context and guide the LLM towards factual, grounded responses.

Large Language Models 113

Large Language Models LLM Prompt Engineer Prompt Engineering 113

AWS Machine Learning Blog

DECEMBER 5, 2023

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

Metadata 118

Metadata Auto-classification Auto-complete Content Enrichment 118

Marktechpost

DECEMBER 2, 2024

However, noise, ambiguity, and deviation in intent in user queries are often a hindrance to effective document retrieval. Query rewriting plays an important role in refining such inputs to ensure that retrieved documents more closely match the actual intent of the user. Don’t Forget to join our 55k+ ML SubReddit.

Large Language Models 43

Large Language Models Prompt Engineer Prompt Engineering Categorization 43

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Operationalization journey per generative AI user type To simplify the description of the processes, we need to categorize the main generative AI user types, as shown in the following figure. Strong domain knowledge for tuning, including prompt engineering, is required as well. We will cover monitoring in a separate post.

Generative AI 122

Generative AI Prompt Engineer Prompt Engineering ML 122

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering. But this approach requires labeled data—and a fair amount of it.

Data Science 64

Data Science Large Language Models Data Scientist Machine Learning 64

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering. But this approach requires labeled data—and a fair amount of it.

Data Science 64

Data Science Large Language Models Data Scientist Machine Learning 64

Snorkel AI

FEBRUARY 5, 2025

RAG systems combine the strengths of reliable source documents with the generative capability of large language models (LLMs). After a user enters their query, the system retrieves relevant documents or document chunks from the vector database and adds them to the initial request as context.

Data Scientist 52

Data Scientist LLM Prompt Engineer Prompt Engineering 52

Marktechpost

JANUARY 11, 2024

We have categorized them to make it easier to cover maximum tools. This allows Bard to serve as a personal AI assistant, aiding in tasks like email responses, content creation, document translation, and meeting note summarization. Mistral 7B : It is a powerful language model, boasting 7.3

Large Language Models 103

Large Language Models Natural Language Processing Artificial Intelligence Artificial Intelligence 103

Towards AI

SEPTEMBER 26, 2023

OpenAI Announces DALL·E 3 OpenAI is launching DALL·E 3, an improved version that excels in following instructions, requires less prompt engineering, and can communicate with ChatGPT. This integration enables users to refine DALL·E 3 prompts by describing their ideas to ChatGPT. Five 5-minute reads/videos to keep you learning 1.Adept.ai

Prompt Engineer 64

Prompt Engineer Prompt Engineering OpenAI ChatGPT 64

The MLOps Blog

SEPTEMBER 26, 2024

Effective mitigation strategies involve enhancing data quality, alignment, information retrieval methods, and prompt engineering. Broadly speaking, we can reduce hallucinations in LLMs by filtering responses, prompt engineering, achieving better alignment, and improving the training data. In 2022, when GPT-3.5

LLM 72

LLM Prompt Engineer Prompt Engineering Auto-complete 72

Unite.AI

AUGUST 4, 2023

In this article, we will delve deeper into these issues, exploring the advanced techniques of prompt engineering with Langchain, offering clear explanations, practical examples, and step-by-step instructions on how to implement them. Prompts play a crucial role in steering the behavior of a model.

Prompt Engineer 245

Prompt Engineer Prompt Engineering Python NLP 245

Bugra Akyildiz

AUGUST 27, 2023

Turbo, including documentation, tutorials, and pre-trained models. LARs are a type of embedding that can be used to represent high-dimensional categorical data in a lower-dimensional continuous space. TypeChat replaces prompt engineering with schema engineering.

OpenAI 52

OpenAI Large Language Models Categorization LLM 52

Snorkel AI

JUNE 23, 2023

This approach was less popular among our attendees from the wealthiest of corporations, who expressed similar levels of interest in fine-tuning with prompts and responses, fine-tuning with unstructured data, and prompt engineering. But this approach requires labeled data—and a fair amount of it.

Data Science 52

Data Science Large Language Models Machine Learning Categorization 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Building the prompt Each predictive task sent to an LLM starts with a prompt template.

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Building the prompt Each predictive task sent to an LLM starts with a prompt template.

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

Snorkel AI

AUGUST 17, 2023

Users can easily constrain an LLM’s output with clever prompt engineering. That minimizes the chance that the prompt will overrun the context window, and also reduces the cost of high-volume runs. Its categorical power is brittle. Building the prompt Each predictive task sent to an LLM starts with a prompt template.

Generative AI 52

Generative AI BERT LLM Prompt Engineer 52

ODSC - Open Data Science

JANUARY 7, 2025

Classification techniques, such as image recognition and document categorization, remain essential for a wide range of industries. Classification techniques like random forests, decision trees, and support vector machines are among the most widely used, enabling tasks such as categorizing data and building predictive models.

Data Scientist 40

Data Scientist Data Science Deep Learning Machine Learning 40

Snorkel AI

MAY 25, 2023

It quickly became the focal point for large language model research (you’ll see it referenced many times in this document), and served as the original underpinning of ChatGPT. Embeddings are also used to represent larger textual units, such as sentences, paragraphs, or entire documents. Most recently, OpenAI debuted GPT-4.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

It quickly became the focal point for large language model research (you’ll see it referenced many times in this document), and served as the original underpinning of ChatGPT. Embeddings are also used to represent larger textual units, such as sentences, paragraphs, or entire documents. Most recently, OpenAI debuted GPT-4.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

DECEMBER 14, 2023

In the image above, you can see on the left that categorical “overlap” is sometimes intentional. For instance, you can first classify documents by type. Then, depending on the type, you can classify a smaller set of intents specifically for that document type. Both can produce unclear distinctions between classes.

Categorization 64

Categorization Data Science Prompt Engineer Prompt Engineering 64

Snorkel AI

DECEMBER 14, 2023

In the image above, you can see on the left that categorical “overlap” is sometimes intentional. For instance, you can first classify documents by type. Then, depending on the type, you can classify a smaller set of intents specifically for that document type. Both can produce unclear distinctions between classes.

Categorization 52

Categorization Data Scientist Prompt Engineer Prompt Engineering 52

Flipboard

DECEMBER 6, 2023

Types of summarizations There are several techniques to summarize text, which are broadly categorized into two main approaches: extractive and abstractive summarization. Given their versatile nature, these models require specific task instructions provided through input text, a practice referred to as prompt engineering.

BERT 166

BERT Large Language Models Artificial Intelligence Artificial Intelligence 166

Unite.AI

SEPTEMBER 20, 2023

To install and import the library, use the following commands: pip install -q transformers from transformers import pipeline Having done that, you can execute NLP tasks starting with sentiment analysis, which categorizes text into positive or negative sentiments.

LLM 342

LLM NLP BERT Python 342

Unite.AI

AUGUST 8, 2023

These advances have fueled applications in document creation, chatbot dialogue systems, and even synthetic music composition. An example would be customizing T5 to generate summaries for documents in a specific industry. Recent Big-Tech decisions underscore its significance.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

Marktechpost

SEPTEMBER 15, 2024

These sources can be categorized into three types: textual documents (e.g., Techniques like Uprise and DaSLaM use lightweight retrievers or small models to optimize prompts, break down complex problems, or generate pseudo labels. KD methods can be categorized into white-box and black-box approaches.

BERT 117

BERT LLM Large Language Models Categorization 117

AWS Machine Learning Blog

NOVEMBER 1, 2023

Key strengths of VLP include the effective utilization of pre-trained VLMs and LLMs, enabling zero-shot or few-shot predictions without necessitating task-specific modifications, and categorizing images from a broad spectrum through casual multi-round dialogues. of OBELICS multimodal web documents. of image-text pairs and 30.7%

Auto-classification 112

Auto-classification LLM Auto-complete Generative AI 112