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.

NLP 98

NLP Natural Language Processing Metadata Large Language Models 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.

LLM 89

LLM Large Language Models Chatbots Natural Language Processing 89

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.

Large Language Models 98

Large Language Models Categorization LLM Natural Language Processing 98

Marktechpost

DECEMBER 12, 2023

Thanks to the success in increasing the data, model size, and computational capacity for auto-regressive language modeling, conversational AI agents have witnessed a remarkable leap in capability in the last few years. In comparison to the more powerful LLMs, this severely restricts their potential.

Generative AI 99

Generative AI AI Modeling Large Language Models LLM 99

Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., rely on Language Models as their foundation. Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model.

Large Language Models 92

Large Language Models BERT Natural Language Processing Categorization 92

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. 

Large Language Models 147

Large Language Models Natural Language Processing Categorization Automation 147

Marktechpost

DECEMBER 6, 2023

Recent months have seen a significant rise in the popularity of Large Language Models (LLMs). Based on the strengths of Natural Language Processing, Natural Language Understanding, and Natural Language Generation, these models have demonstrated their capabilities in almost every industry.

Large Language Models 96

Large Language Models LLM Artificial Intelligence Artificial Intelligence 96

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). paws : A dataset tailor-made for paraphrase identification, a crucial natural language understanding component.

LLM 52

LLM NLP Large Language Models Categorization 52

Snorkel AI

JUNE 23, 2023

As indicated in the previous section, a majority of our respondents—and all of the respondents from companies worth at least a billion dollars—believe that off-the-shelf LLMs won’t be up to their enterprise-specific tasks and will require fine-tuning or pre-training.

Data Science 64

Data Science Large Language Models Data Scientist Categorization 64

Snorkel AI

JUNE 23, 2023

As indicated in the previous section, a majority of our respondents—and all of the respondents from companies worth at least a billion dollars—believe that off-the-shelf LLMs won’t be up to their enterprise-specific tasks and will require fine-tuning or pre-training.

Data Science 64

Data Science Large Language Models Data Scientist Categorization 64

John Snow Labs

OCTOBER 19, 2023

How to use the LangTest library to evaluate LLM for bias using CrowS-Pairs dataset? Natural language processing (NLP) is a critical branch of artificial intelligence devoted to understanding and generating natural language. Some example samples. We can check the detailed results using .generated_results()

BERT 52

BERT Natural Language Processing NLP Categorization 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. The diagram visualizes the architecture of an AI system powered by a Large Language Model and Agents.

Auto-complete 240

Auto-complete ChatGPT Large Language Models AI 240

IBM Journey to AI blog

APRIL 18, 2024

AI systems like LaMDA and GPT-3 excel at generating human-quality text, accomplishing specific tasks, translating languages as needed, and creating different kinds of creative content. Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles.

Neural Network 239

Neural Network LLM AI AI 239

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., First, they can select a pretrained LLM that demonstrates acceptable performance for their use case. a social media post or product description).

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

Snorkel AI

AUGUST 24, 2023

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff. Tracking the status of complaints.

Large Language Models 52

Large Language Models AI AI Natural Language Processing 52

Snorkel AI

AUGUST 24, 2023

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff. Tracking the status of complaints.

Large Language Models 52

Large Language Models AI AI Natural Language Processing 52

Snorkel AI

AUGUST 24, 2023

AI is accelerating complaint resolution for banks AI can help banks automate many of the tasks involved in complaint handling, such as: Identifying, categorizing, and prioritizing complaints. Natural language processing to extract key information quickly. Assigning complaints to staff. Tracking the status of complaints.

Large Language Models 52

Large Language Models AI AI Natural Language Processing 52

AWS Machine Learning Blog

APRIL 27, 2023

Solution overview With the onset of large language models, the field has seen tremendous progress on various natural language processing (NLP) benchmarks. We also convert the categorical variable t into a natural language sentence. We used Amazon SageMaker Ground Truth Plus in our data collection.

Machine Learning 65

Machine Learning Large Language Models Categorization Natural Language Processing 65

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.

NLP 64

NLP Categorization Natural Language Processing BERT 64

AWS Machine Learning Blog

SEPTEMBER 1, 2023

Furthermore, we deep dive on the most common generative AI use case of text-to-text applications and LLM operations (LLMOps), a subset of FMOps. 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.

Generative AI 103

Generative AI Prompt Engineer Prompt Engineering AI 103

Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

LLM 135

LLM NLP Large Language Models OpenAI 135

Snorkel AI

JULY 19, 2023

We validated the downstream effect of fine-tuning on this higher-quality data with the Together AI fine-tuning service (now available via API ), which we used to create an improved version of the open-source RedPajama chat LLM with full data transparency. For these experiments, we use the RedPajama family of LLMs.

Categorization 52

Categorization ChatGPT Large Language Models Generative AI 52

Snorkel AI

JULY 19, 2023

We validated the downstream effect of fine-tuning on this higher-quality data with the Together AI fine-tuning service (now available via API ), which we used to create an improved version of the open-source RedPajama chat LLM with full data transparency. For these experiments, we use the RedPajama family of LLMs.

Categorization 52

Categorization ChatGPT Large Language Models Generative AI 52

Topbots

JULY 24, 2023

The strategies presented in this article, are primarily relevant for developers building large language model (LLM) applications. Instances of unsatisfactory results from a language model are often due to vague instructions. Contrary to popular belief, brevity isn’t synonymous with specificity in LLM prompts.

Prompt Engineer 98

Prompt Engineer Prompt Engineering LLM ChatGPT 98

Snorkel AI

JULY 19, 2023

We validated the downstream effect of fine-tuning on this higher-quality data with the Together AI fine-tuning service (now available via API ), which we used to create an improved version of the open-source RedPajama chat LLM with full data transparency. For these experiments, we use the RedPajama family of LLMs.

Categorization 40

Categorization ChatGPT Large Language Models Generative AI 40

Topbots

JULY 6, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. Q-former is pre-trained with a new two-stage strategy: The first pre-training stage performs vision-language representation learning. What are the results?

LLM 52

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.

LLM 107

LLM Natural Language Processing Prompt Engineer Prompt Engineering 107

Explosion

FEBRUARY 15, 2023

As many large financial institutions push to use Natural Language Processing (NLP) to digitize their customer support channels, smaller financial institutions like credit unions and community banks are having a tough time to keep pace. Want to learn how to customize Prodigy for efficient chatbot annotations?

Chatbots 59

Chatbots NLP Data Scientist OpenAI 59

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. Model Training: Fine-tune a powerful open-source LLM such as Mistral on the synthetic data using contrastive loss.

Large Language Models 147

Large Language Models Prompt Engineer Prompt Engineering BERT 147

AWS Machine Learning Blog

MAY 7, 2024

We also examine the uplift from fine-tuning an LLM for a specific extractive task. Whether you’re looking to classify documents, extract keywords, detect and redact personally identifiable information (PIIs), or parse semantic relationships, you can start ideating your use case and use LLMs for your natural language processing (NLP).

Prompt Engineer 95

Prompt Engineer Prompt Engineering Large Language Models LLM 95

Snorkel AI

MAY 25, 2023

in the linked article) in a process that results not only in a set of valuable weights for the model, but also an embedding for each word fed to it. At inference time, users provide “prompts” to the LLM—snippets of text that the model uses as a jumping-off point. A week later, Google introduced Bard, its own LLM-backed chatbot.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

in the linked article) in a process that results not only in a set of valuable weights for the model, but also an embedding for each word fed to it. At inference time, users provide “prompts” to the LLM—snippets of text that the model uses as a jumping-off point. A week later, Google introduced Bard, its own LLM-backed chatbot.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

AWS Machine Learning Blog

SEPTEMBER 14, 2023

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text. Knowledge management – Categorizing documents in a systematic way helps to organize an organization’s knowledge base. This allows for better monitoring and auditing.

Categorization 95

Categorization Machine Learning Data Scientist Natural Language Processing 95

Unite.AI

JANUARY 17, 2024

In this article, we will discuss PowerInfer, a high-speed LLM inference engine designed for standard computers powered by a single consumer-grade GPU. The PowerInfer framework seeks to utilize the high locality inherent in LLM inference, characterized by a power-law distribution in neuron activations.

Large Language Models 130

Large Language Models Inference Engine LLM Generative AI 130

IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. A specific kind of foundation model known as a large language model (LLM) is trained on vast amounts of text data for NLP tasks. An open-source model, Google created BERT in 2018.

Generative AI 151

Generative AI Data Scientist BERT Machine Learning 151

The MLOps Blog

FEBRUARY 22, 2024

Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks. It is designed for PyTorch and can be used in various domains like Computer Vision and Natural Language Processing. Renate is a library designed by the AWS Labs.

Continuous Learning 59

Continuous Learning Machine Learning ML Neural Network 59

AWS Machine Learning Blog

MARCH 28, 2024

To address these challenges, parent document retrievers categorize and designate incoming documents as parent documents. These documents are recognized for their comprehensive nature but aren’t directly utilized in their original form for embeddings. During retrieval, the parent document is invoked. Create a question embedding.

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LLM Auto-complete Auto-classification Generative AI 111