Remove Data Scarcity Remove Large Language Models Remove NLP
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Leveraging Linguistic Expertise in NLP: A Deep Dive into RELIES and Its Impact on Large Language Models

Marktechpost

With the significant advancement in the fields of Artificial Intelligence (AI) and Natural Language Processing (NLP), Large Language Models (LLMs) like GPT have gained attention for producing fluent text without explicitly built grammar or semantic modules.

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Advancing Cantonese NLP: Bridging Development Gaps in Large Language Models with New Benchmarks and Open-Source Innovations

Marktechpost

Large language models (LLMs) have revolutionized natural language processing (NLP), particularly for English and other data-rich languages. However, this rapid advancement has created a significant development gap for underrepresented languages, with Cantonese being a prime example.

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Innovations in Analytics: Elevating Data Quality with GenAI

Towards AI

GenAI can help by automatically clustering similar data points and inferring labels from unlabeled data, obtaining valuable insights from previously unusable sources. Natural Language Processing (NLP) is an example of where traditional methods can struggle with complex text data. GPT-4o mini response use case #2.

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NeoBERT: Modernizing Encoder Models for Enhanced Language Understanding

Marktechpost

Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. Data Scarcity: Pre-training on small datasets (e.g., Wikipedia + BookCorpus) restricts knowledge diversity.

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Innovation in Synthetic Data Generation: Building Foundation Models for Specific Languages

Unite.AI

Synthetic data , artificially generated to mimic real data, plays a crucial role in various applications, including machine learning , data analysis , testing, and privacy protection. However, generating synthetic data for NLP is non-trivial, demanding high linguistic knowledge, creativity, and diversity.

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Unpacking the NLP Summit: The Promise and Challenges of Large Language Models

John Snow Labs

The recent NLP Summit served as a vibrant platform for experts to delve into the many opportunities and also challenges presented by large language models (LLMs). Strategy and Data: Non-top-performers highlight strategizing (24%), talent availability (21%), and data scarcity (18%) as their leading challenges.

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This AI Paper from Cohere for AI Presents a Comprehensive Study on Multilingual Preference Optimization

Marktechpost

Multilingual natural language processing (NLP) is a rapidly advancing field that aims to develop language models capable of understanding & generating text in multiple languages. These models facilitate effective communication and information access across diverse linguistic backgrounds.