Artificial Corner

JULY 30, 2023

In this world of complex terminologies, someone who wants to explain Large Language Models (LLMs) to some non-tech guy is a difficult task. So that’s why I tried in this article to explain LLM in simple or to say general language. No training examples are needed in LLM Development but it’s needed in Traditional Development.

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Large Language Models LLM Natural Language Processing Deep Learning 85

John Snow Labs

JUNE 26, 2024

This blog post explores how John Snow Labs’ Healthcare NLP models are revolutionizing the extraction of critical insights on opioid use disorder. Here, NLP offers a powerful solution. Let us start with a short Spark NLP introduction and then discuss the details of opioid drugs analysis with some solid results.

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NLP Categorization Large Language Models Data Analysis 52

Marktechpost

JULY 19, 2024

Therefore, text-to-SQL research can benefit from the unique opportunities, enhancements, and solutions that can be brought about by integrating LLM-based implementation, such as improved query accuracy, better handling of complex queries, and increased system robustness. Join our Telegram Channel and LinkedIn Gr oup.

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LLM Neural Network Large Language Models Natural Language Processing 105

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. From customer service and ecommerce to healthcare and finance, the potential of LLMs is being rapidly recognized and embraced.

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Automation Prompt Engineer Prompt Engineering Categorization 119

Snorkel AI

DECEMBER 27, 2023

Thus, it’s important to remember that the latest and greatest in LLM tech is built upon years of prior research, and many of the previous generation of models, especially Google’s BERT, still provide great performance at a lower cost. This makes BERT one of the original LLM architectures. It’s also one of the simplest.

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Marktechpost

APRIL 30, 2024

Previous studies have proposed that LLMs demonstrate considerable generalization abilities, allowing them to apply learned knowledge to new tasks not encountered during training, a phenomenon known as zero-shot learning. However, fine-tuning remains crucial to optimize LLM performance on robust user datasets and tasks.

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Large Language Models Categorization Natural Language Processing Algorithm 127

John Snow Labs

APRIL 18, 2024

In this post, we explore the utilization of pre-trained models within the Healthcare NLP library by John Snow Labs to map medical terminology to the MedDRA ontology. Specifically, our aim is to facilitate standardized categorization for enhanced medical data analysis and interpretation. that map clinical terms to MedDRA codes.

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Marktechpost

JULY 7, 2023

They considered all the projects that fit these criteria: Projects must have been created eight months ago or less (approx November 2022, to June 2023, at the time of this paper’s publication) Projects are related to the topics: LLM, ChatGPT, Open-AI, GPT-3.5, or GPT-4 Projects must have at least 3,000 stars on GitHub.

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Marktechpost

MAY 9, 2024

In Natural Language Processing (NLP) tasks, data cleaning is an essential step before tokenization, particularly when working with text data that contains unusual word separations such as underscores, slashes, or other symbols in place of spaces. The post Is There a Library for Cleaning Data before Tokenization?

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NLP Natural Language Processing Metadata Large Language Models 108

Heartbeat

FEBRUARY 13, 2024

Learning Path to Building LLM-Based Solutions — For Practitioner Data Scientists As everyone would agree, the advent of LLM has transformed the technology industry, and technocrats have had a huge surge of interest in learning about LLMs. Langchain is very comprehensive, and its applications are evolving rapidly.

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Data Scientist LLM Deep Learning Prompt Engineer 52

John Snow Labs

NOVEMBER 5, 2024

This blog post explores how John Snow Labs’ Healthcare NLP & LLM library is transforming clinical trials by using advanced NER models to efficiently filter through large datasets of patient records. link] John Snow Labs’ Healthcare NLP & LLM library offers a powerful solution to streamline this process.

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NLP LLM Large Language Models Categorization 52

Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text.

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Data Extraction Neural Network Large Language Models NLP 273

Unite.AI

JANUARY 11, 2024

They serve as a core building block in many natural language processing (NLP) applications today, including information retrieval, question answering, semantic search and more. vector embedding Recent advances in large language models (LLMs) like GPT-3 have shown impressive capabilities in few-shot learning and natural language generation.

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Large Language Models Prompt Engineer Prompt Engineering BERT 147

Marktechpost

MARCH 10, 2024

As the complexity of human language and the demands on LLMs to mirror this complexity grow, the quest for comprehensive and varied datasets has led researchers to pioneer innovative methods for dataset creation and optimization, aiming to capture the multifaceted nature of language across various contexts and domains.

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Large Language Models Natural Language Processing LLM Categorization 109

Topbots

JULY 24, 2023

The strategies presented in this article, are primarily relevant for developers building large language model (LLM) applications. Contrary to popular belief, brevity isn’t synonymous with specificity in LLM prompts. Let’s explore some tactics to guide an LLM towards step-by-step thinking and problem solving.

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Prompt Engineer Prompt Engineering LLM ChatGPT 98

Towards AI

MAY 20, 2023

This new era of custom LLMs marks a significant milestone in the quest for more customizable and efficient language processing solutions. Challenges in building custom LLMs include architecture selection, data quality, bias mitigation, content moderation, resource management, and expertise.

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LLM Large Language Models Chatbots Natural Language Processing 82

John Snow Labs

OCTOBER 19, 2023

Generating Synthetic NLP Data to Reduce Sycophancy In our continued pursuit of taming sycophantic behavior in AI models on mathematical data, we focus on Natural Language Processing (NLP). Our toolkit of NLP datasets is extensive, encompassing a wide spectrum of datasets that can be defined in the harness class.

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LLM NLP Large Language Models Categorization 52

Marktechpost

OCTOBER 4, 2024

Natural language processing (NLP) has seen rapid advancements, with large language models (LLMs) leading the charge in transforming how text is generated and interpreted. This complexity must be fully captured in current hallucination evaluation benchmarks, making assessing LLM performance in such nuanced situations challenging.

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LLM Natural Language Processing Large Language Models Categorization 113

Marktechpost

APRIL 26, 2024

Also, the advent of LLMs has sparked innovation in graph learning, with models like GraphGPT and GLEM utilizing advanced language model techniques to understand and manipulate graph structures effectively. Foundation Models (FMs) have revolutionized NLP and vision domains in the broader AI spectrum.

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Marktechpost

AUGUST 15, 2023

Large language models (LLMs) have achieved amazing results in a variety of Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks in recent years.

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Large Language Models Categorization LLM Natural Language Processing 98

Marktechpost

JANUARY 4, 2024

Techniques like checkpointing, designed to save the training state periodically, and strategies including elastic training and redundant computation, mainly address individual aspects of LLM training failures. Unicron’s methodology is an embodiment of innovation in LLM training resilience. Check out the Paper.

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Computational Linguistics Large Language Models LLM BERT 122

Unite.AI

SEPTEMBER 13, 2023

Industry Anomaly Detection and Large Vision Language Models Existing IAD frameworks can be categorized into two categories. LLMs or Large Language Models have enjoyed tremendous success in the NLP industry, and they are now being explored for their applications in visual tasks. Reconstruction-based IAD.

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Convolutional Neural Networks LLM Neural Network Large Language Models 288

IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018. All watsonx.ai

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Generative AI BERT Data Scientist Machine Learning 189

Marktechpost

JULY 7, 2024

Significant progress has been made in natural language processing (NLP) and information retrieval (IR) technologies. Combining LLMs with search engine services is an exciting new area in services computing, that can greatly enhance search functionalities and change how users interact with digital information systems.

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Natural Language Processing Large Language Models Categorization LLM 108

AWS Machine Learning Blog

JUNE 18, 2024

It examines how AI can optimize financial workflow processes by automatically summarizing documents, extracting data, and categorizing information from email attachments. There may occasionally be different sorts of documents and no automatic method for identifying and categorizing them. page_content) print(summary.replace(" ","").strip())

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Automation IDP Generative AI Data Extraction 115

John Snow Labs

SEPTEMBER 17, 2024

Let’s start with a brief introduction to Spark NLP and then discuss the details of the Stigmatization NER model with some concrete results. Healthcare NLP & LLM The Healthcare Library is a powerful component of John Snow Labs’ Spark NLP platform, designed to facilitate NLP tasks within the healthcare domain.

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NLP Categorization AI AI 52

Snorkel AI

OCTOBER 27, 2023

Snorkel AI held its Enterprise LLM Virtual Summit on October 26, 2023, drawing an engaged crowd of more than 1,000 attendees across three hours and eight sessions that featured 11 speakers. The first categorizes instructions, while the second assesses the quality of responses. See what Snorkel option is right for you.

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LLM Data Scientist Machine Learning Large Language Models 52

Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Transformers and Advanced NLP Models : The introduction of transformer architectures revolutionized the NLP landscape.

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John Snow Labs

APRIL 22, 2024

While large language models (LLMs) have demonstrated impressive capabilities in various natural language processing (NLP) tasks, their performance in this domain has been limited by the inherent complexities of medical language and the nuances involved in interpreting clinical narratives.

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NLP Categorization Natural Language Processing BERT 64

Viso.ai

JULY 18, 2024

If a computer program is trained on enough data such that it can analyze, understand, and generate responses in natural language and other forms of content, it is called a Large Language Model (LLM). An easy way to describe LLM is an AI algorithm capable of understanding and generating human language.

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Large Language Models Neural Network Natural Language Processing BERT 52

AssemblyAI

JANUARY 23, 2024

  By leveraging Deep Learning architectures and training on vast amounts of data, LLMs can process and understand more nuance and context in human language than traditional Natural Language Processing (NLP) models. 

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Large Language Models Natural Language Processing Categorization Automation 147

AssemblyAI

MAY 14, 2024

Speech AI technology (including Speech-to-Text, Audio Intelligence, and LLM capabilities ) has quickly become an integral part of thousands of organizations and developer workflows. It automatically categorizes, summarizes, and extracts actionable insights from customer calls, such as flagging questions and complaints.

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Natural Language Processing Automation Robotics Categorization 147

AWS Machine Learning Blog

SEPTEMBER 13, 2024

Using natural language processing (NLP) and OpenAPI specs, Amazon Bedrock Agents dynamically manages API sequences, minimizing dependency management complexities. This agent invokes a Lambda function that internally calls the Anthropic Claude Sonnet large language model (LLM) on Amazon Bedrock to perform preliminary analysis on the images.

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Marktechpost

DECEMBER 6, 2023

RAG enhances the capabilities of LLM by obtaining pertinent data from other sources, which is perfect for applications that query documents, databases, or other structured or unstructured data repositories. On the other hand, Fine-tuning lacks the guarantee of recall, making it less reliable. How do use cases differ for RAG and Fine-tuning?

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Large Language Models Artificial Intelligence Artificial Intelligence Natural Language Processing 105

Topbots

JULY 6, 2023

To test this suggestion, they trained a 175B-parameter autoregressive language model, called GPT-3 , and evaluated its performance on over two dozen NLP tasks. The second pre-training stage performs vision-to-language generative learning by connecting the output of the Q-Former to a frozen LLM. What are the results?

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LLM Large Language Models Natural Language Processing OpenAI 52

AWS Machine Learning Blog

MARCH 13, 2024

The LLM analysis provides a violation result (Y or N) and explains the rationale behind the model’s decision regarding policy violation. The audio moderation workflow activates the LLM’s policy evaluation only when the toxicity analysis exceeds a set threshold. The LLM model returns an analysis result based on the prompt instructions.

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LLM Natural Language Processing Prompt Engineer Prompt Engineering 125

Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., Applications of LLMs The chart below summarises the present state of the Large Language Model (LLM) landscape in terms of features, products, and supporting software.

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