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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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Best Large Language Models & Frameworks of 2023

AssemblyAI

However, among all the modern-day AI innovations, one breakthrough has the potential to make the most impact: large language models (LLMs). These feats of computational linguistics have redefined our understanding of machine-human interactions and paved the way for brand-new digital solutions and communications.

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Computational Linguistic Analysis of Engineered Chatbot Prompts

John Snow Labs

Therefore, it is important to analyze and understand the linguistic features of effective chatbot prompts for education. In this paper, we present a computational linguistic analysis of chatbot prompts used for education.

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Large Language Models – Technical Overview

Viso.ai

What are Large Language Models (LLMs)? In generative AI, human language is perceived as a difficult data type. 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).

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Do Large Language Models Really Need All Those Layers? This AI Research Unmasks Model Efficiency: The Quest for Essential Components in Large Language Models

Marktechpost

The advent of large language models (LLMs) has sparked significant interest among the public, particularly with the emergence of ChatGPT. These models, which are trained on extensive amounts of data, can learn in context, even with minimal examples. Strikingly, even after removing up to 70% (around 15.7

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Linguistics-aware In-context Learning with Data Augmentation (LaiDA): An AI Framework for Enhanced Metaphor Components Identification in NLP Tasks

Marktechpost

Metaphor Components Identification (MCI) is an essential aspect of natural language processing (NLP) that involves identifying and interpreting metaphorical elements such as tenor, vehicle, and ground. This framework leverages the power of large language models (LLMs) like ChatGPT to improve the accuracy and efficiency of MCI.

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Overcoming The Limitations Of Large Language Models

Topbots

In the last couple of years, Large Language Models (LLMs) such as ChatGPT, T5 and LaMDA have developed amazing skills to produce human language. We are quick to attribute intelligence to models and algorithms, but how much of this is emulation, and how much is really reminiscent of the rich language capability of humans?