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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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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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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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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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The State of Multilingual AI

Sebastian Ruder

Models that allow interaction via natural language have become ubiquitious. Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. Vulić, I., & Søgaard, A.

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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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ML and NLP Research Highlights of 2021

Sebastian Ruder

In speech, new models have been built based on wav2vec 2.0 [6] 6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. At the same time, we saw new unified pre-trained models for previously under-researched modality pairs such as for videos and language [9] as well as speech and language [10].

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