AWS Machine Learning Blog

MARCH 18, 2025

Intelligent insights and recommendations Using its large knowledge base and advanced natural language processing (NLP) capabilities, the LLM provides intelligent insights and recommendations based on the analyzed patient-physician interaction. These data sources provide contextual information and serve as a knowledge base for the LLM.

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LLM NLP Data Integration AI 102

Unite.AI

SEPTEMBER 30, 2024

Test Management Tools TestRail integrates AI to streamline test management by generating test cases through NLP. Its AI-driven quality risk analysis recommends tests for high-risk areas, ensuring that critical issues are covered. It goes one step further and prioritizes each test case based on risk.

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Automation AI AI DevOps 312

Marktechpost

MARCH 6, 2025

A critical challenge in multilingual NLP is the uneven distribution of linguistic resources. High-resource languages benefit from extensive corpora, while languages spoken in developing regions often lack sufficient training data.

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Large Language Models LLM NLP Data Quality 67

Marktechpost

SEPTEMBER 14, 2023

In the ever-evolving field of Natural Language Processing (NLP), the development of machine translation and language models has been primarily driven by the availability of vast training datasets in languages like English. This limitation hampers the progress of NLP technologies for a wide range of linguistic communities worldwide.

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AI Researcher AI Research Natural Language Processing NLP 130

Marktechpost

JUNE 10, 2024

The emergence of large language models (LLMs) such as Llama, PaLM, and GPT-4 has revolutionized natural language processing (NLP), significantly advancing text understanding and generation. Understanding hallucinations’ various types and underlying causes is crucial for developing effective mitigation strategies.

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Large Language Models Categorization Data Quality Natural Language Processing 141

Pickl AI

OCTOBER 18, 2023

How to Scale Your Data Quality Operations with AI and ML: In the fast-paced digital landscape of today, data has become the cornerstone of success for organizations across the globe. Every day, companies generate and collect vast amounts of data, ranging from customer information to market trends.

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Data Quality ML Machine Learning Natural Language Processing 52

Marktechpost

SEPTEMBER 30, 2023

InternLM-20B represents a significant leap forward in language model architecture and training data quality. This depth empowers the model to excel in language understanding, a crucial aspect of NLP. What truly sets InternLM-20B apart is its training data.

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Artificial Intelligence Artificial Intelligence Natural Language Processing NLP 122

O'Reilly Media

NOVEMBER 19, 2024

at Google, and “ Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ” by Patrick Lewis, et al., For example, a mention of “NLP” might refer to natural language processing in one context or neural linguistic programming in another. Chunk your documents from unstructured data sources, as usual in GraphRAG.

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LLM NLP Hybrid AI Large Language Models 125

John Snow Labs

OCTOBER 10, 2023

In this presentation, we delve into the effective utilization of Natural Language Processing (NLP) agents in the context of Acciona. We explore a range of practical use cases where NLP has been deployed to enhance various processes and interactions.

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NLP Natural Language Processing Data Extraction Data Quality 52

Marktechpost

NOVEMBER 10, 2023

NLP, or Natural Language Processing, is a field of AI focusing on human-computer interaction using language. NLP aims to make computers understand, interpret, and generate human language. Recent NLP research has focused on improving few-shot learning (FSL) methods in response to data insufficiency challenges.

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BERT ChatGPT Large Language Models NLP 126

Chatbots Life

SEPTEMBER 11, 2023

This limitation has paved the way for more advanced solutions that harness the power of Natural Language Processing (NLP). This has spurred the development of more advanced solutions powered by Natural Language Processing (NLP) that offer a more comprehensive approach to language-related tasks.

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NLP Natural Language Processing Python Algorithm 59

Marktechpost

MAY 3, 2024

These models have played an important role in this dynamic field by influencing natural language processing (NLP) significantly. AI’s Yi models that focus on data quality. This paper highlights the most influential open-source LLMs like Mistral’s sparse Mixture of Experts model Mixtral-8x7B, Alibaba Cloud’s multilingual Qwen1.5

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Artificial Intelligence Artificial Intelligence Large Language Models Natural Language Processing 131

Unite.AI

OCTOBER 12, 2023

Our customers are working on a wide range of applications, including augmented and virtual reality, computer vision , conversational AI, generative AI, search relevance and speech and natural language processing (NLP), among others.

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Machine Learning Deep Learning Conversational AI Data Quality 273

Flipboard

JUNE 17, 2023

In particular, Tart achieves the necessary goals: • Task-neutral: Tart’s inference module must be trained once with fictitious data. Quality: Performs better than basic LLM across the board and closes the gap using task-specific fine-tuning techniques. Data-scalable: Handling 10 times as many instances as in-context learning.

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LLM Large Language Models NLP Prompt Engineer 145

Pickl AI

MARCH 4, 2025

Unlike traditional AI, which operates within predefined rules and tasks, It uses advanced technologies like Machine Learning, Natural Language Processing (NLP) , and Large Language Models (LLMs) to navigate complex, dynamic environments. It uses Natural Language Processing (NLP) to facilitate seamless communication between humans and AI.

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Natural Language Processing NLP Automation Artificial Intelligence 52

Flipboard

FEBRUARY 19, 2025

The retrieval component uses Amazon Kendra as the intelligent search service, offering natural language processing (NLP) capabilities, machine learning (ML) powered relevance ranking, and support for multiple data sources and formats. Focus should be placed on data quality through robust validation and consistent formatting.

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Responsible AI Natural Language Processing Explainability Large Language Models 81

Marktechpost

FEBRUARY 29, 2024

Extremely low-resource languages need more labeled data, widening the gap in NLP progress compared to high-resource languages. Lexicon-based cross-lingual data augmentation involves swapping words in high-resource language data with their translations from bilingual lexicons to generate data for low-resource languages.

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Artificial Intelligence Artificial Intelligence Data Scarcity NLP 99

Marktechpost

JUNE 7, 2024

Zyda amalgamates several high-quality open datasets, refining them through rigorous filtering and deduplication. The result is a dataset that boasts an impressive token count and maintains the highest data quality standards. This aligns with Zyphra’s commitment to fostering open research and collaboration in NLP.

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NLP Data Quality Large Language Models Artificial Intelligence 107

Heartbeat

AUGUST 23, 2023

Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. The seemingly impossible chore of sorting through mountains of legal documents can be accomplished with astonishing efficiency and precision using NLP.

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NLP Natural Language Processing Algorithm Categorization 52

John Snow Labs

JUNE 28, 2023

John Snow Labs Debuts Comprehensive Healthcare Data Library on Databricks Marketplace: Over 2,400 Expertly Curated, Clean, and Enriched Datasets Now Accessible, Amplifying Data Science Capabilities in Healthcare and Life Sciences.

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Data Scientist NLP Metadata Data Quality 98

John Snow Labs

SEPTEMBER 4, 2023

The field of Natural Language Processing (NLP) has been greatly impacted by the advancements in machine learning, leading to a significant improvement in linguistic understanding and generation. However, new challenges have emerged with the development of these powerful NLP models. Is Your NLP Model Truly Robust?

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NLP Automation Natural Language Processing Large Language Models 52

Snorkel AI

JANUARY 11, 2023

To do this, Pixability had trained a natural language processing (NLP) model to classify videos automatically, yet the performance wasn’t strong enough. Goal Minimize the time spent labeling high-cardinality training data while expanding their ability to provide more granular insights to their customers.

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NLP Auto-classification Categorization Natural Language Processing 52

John Snow Labs

OCTOBER 19, 2023

Understanding the Impact of Bias on NLP Models Why test NLP models for Bias? Natural Language Processing (NLP) models rely heavily on bias to function effectively. This is due to the fact that bias helps NLP models to identify important features and relationships among data points.

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NLP Natural Language Processing Machine Learning AI 52

Towards AI

JUNE 12, 2024

Word embedding is a technique in natural language processing (NLP) where words are represented as vectors in a continuous vector space. This facilitates various NLP tasks by providing meaningful word embeddings. This piece compares and contrasts between the two models. The story starts with word embedding. What is Word Embedding?

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BERT Neural Network Natural Language Processing NLP 104

Pickl AI

NOVEMBER 24, 2024

Denoising Autoencoders (DAEs) Denoising autoencoders are trained on corrupted versions of the input data. The model learns to reconstruct the original data from this noisy input, making them effective for tasks like image denoising and signal processing. They help improve data quality by filtering out noise.

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Deep Learning Neural Network Natural Language Processing Computer Vision 52

Marktechpost

NOVEMBER 20, 2024

Natural Language Processing (NLP) Entity Annotation: Tagging entities like names, dates, or locations. Speech-to-Text Alignment: Transcript creation for NLP processing is known as speech-to-text alignment. Advantages of Data Labeling Better Predictions: Accurate models are the outcome of high-quality labeling.

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Natural Language Processing Computer Vision Machine Learning NLP 103

Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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NLP ML BERT Computational Linguistics 52

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. With further research intoprompt engineering and synthetic data quality, this methodology could greatly advance multilingual text embeddings.

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Large Language Models Prompt Engineer Prompt Engineering BERT 144

Marktechpost

JANUARY 9, 2025

Multilingual applications and cross-lingual tasks are central to natural language processing (NLP) today, making robust embedding models essential. However, existing models often struggle with noisy training data, limited domain diversity, and inefficiencies in managing multilingual datasets. and released under the MIT license.

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Natural Language Processing BERT NLP LLM 82

Google Research AI blog

MARCH 30, 2023

The initial version of DataPerf consists of four challenges focused on three common data-centric tasks across three application domains; vision, speech and natural language processing (NLP). Training dataset evaluation (NLP) : Quality datasets can be expensive to construct, and are becoming valuable commodities.

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ML Algorithm NLP Neural Network 96

Google Research AI blog

MARCH 27, 2023

In the natural language processing (NLP) literature, this is mainly framed as a task-oriented dialogue parsing task, where a given dialogue needs to be parsed by a system to understand the user intent and carry out the operation to fulfill that intent. Examples of utterances in English, Japanese, and French with filler words or repetitions.

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NLP Natural Language Processing Software Engineer Data Quality 98

Marktechpost

JULY 18, 2024

Data engineering is crucial in today’s digital landscape as organizations increasingly rely on data-driven insights for decision-making. Learning data engineering ensures proficiency in designing robust data pipelines, optimizing data storage, and ensuring data quality.

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ETL Python Machine Learning Categorization 110

Unite.AI

OCTOBER 22, 2024

At Appen, we work at the intersection of AI and data, and my experience has allowed me to lead the company and navigate complexities in the rapidly evolving AI space, moving through major developments like voice recognition, NLP, recommendation systems, and now generative AI. Data quality plays a crucial role in AI model development.

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Natural Language Processing Generative AI Computer Vision Data Quality 130

Unite.AI

JUNE 13, 2024

In the domain of Artificial Intelligence (AI) , workflows are essential, connecting various tasks from initial data preprocessing to the final stages of model deployment. This foundational step requires clean and well-structured data to facilitate accurate model training. Next, efficient model training is critical.

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Natural Language Processing Robotics Algorithm AI 144

Marktechpost

JULY 23, 2024

AI and ML applications have improved data quality, rigor, detection, and chemical identification, facilitating major disease screening and diagnosis findings. This process involves matching m/z and MS/MS fragmentation data to confirm metabolites.

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Machine Learning ML Algorithm Data Extraction 103

Towards AI

MAY 20, 2023

Challenges of building custom LLMs Building custom Large Language Models (LLMs) presents an array of challenges to organizations that can be broadly categorized under data, technical, ethical, and resource-related issues. Acquiring a significant volume of domain-specific data can be challenging, especially if the data is niche or sensitive.

LLM 98

LLM Large Language Models Chatbots Natural Language Processing 98

John Snow Labs

SEPTEMBER 26, 2024

Dandelion Health is a provider of multimodal, longitudinal clinical data for healthcare innovators. This session shows how it built a de-identification process for free-text clinical notes, with John Snow Labs’ Healthcare NLP & LLM at its core. Breaking down different note types (e.g.

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NLP LLM Data Quality Large Language Models 52