Remove Data Scarcity Remove Large Language Models Remove Natural Language Processing
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Siddhant Masson, CEO and Co-Founder of Wokelo – Interview Series

Unite.AI

Using proprietary large language model (LLM)-based agents, the platform facilitates the curation, synthesis, and triangulation of data to generate structured, decision-ready outputs.

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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. If you like our work, you will love our newsletter.

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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.

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LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data

Marktechpost

Large language models (LLMs) are at the forefront of technological advancements in natural language processing, marking a significant leap in the ability of machines to understand, interpret, and generate human-like text. Similarly, on the CaseHOLD dataset, there was a 32.6% enhancement, and on SNIPS, a 32.0%

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The Rise of Domain-Specific Language Models

Unite.AI

Introduction The field of natural language processing (NLP) and language models has experienced a remarkable transformation in recent years, propelled by the advent of powerful large language models (LLMs) like GPT-4, PaLM, and Llama.

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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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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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