Remove BERT Remove Computational Linguistics Remove Large Language Models
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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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Alibaba Researchers Unveil Unicron: An AI System Designed for Efficient Self-Healing in Large-Scale Language Model Training

Marktechpost

The development of Large Language Models (LLMs), such as GPT and BERT, represents a remarkable leap in computational linguistics. Training these models, however, is challenging.

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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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This AI Paper from Cohere Enhances Language Model Stability with Automated Detection of Under-trained Tokens in LLMs

Marktechpost

Tokenization is essential in computational linguistics, particularly in the training and functionality of large language models (LLMs). This process involves dissecting text into manageable pieces or tokens, which is foundational for model training and operations.

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Stanford AI Lab Papers and Talks at ACL 2022

The Stanford AI Lab Blog

The 60th Annual Meeting of the Association for Computational Linguistics (ACL) 2022 is taking place May 22nd - May 27th. We’re excited to share all the work from SAIL that’s being presented, and you’ll find links to papers, videos and blogs below.

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All Languages Are NOT Created (Tokenized) Equal

Topbots

Large language models such as ChatGPT process and generate text sequences by first splitting the text into smaller units called tokens. Language Disparity in Natural Language Processing This digital divide in natural language processing (NLP) is an active area of research. Shijie Wu and Mark Dredze.

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68 Summaries of Machine Learning and NLP Research

Marek Rei

It is probably good to also to mention that I wrote all of these summaries myself and they are not generated by any language models. Are Emergent Abilities of Large Language Models a Mirage? Do Large Language Models Latently Perform Multi-Hop Reasoning? Here we go. NeurIPS 2023. ArXiv 2024.