Topbots

JUNE 15, 2023

Discussion Implications of tokenization language disparity Overall, requiring more tokens (to tokenize the same message in a different language) means: You’re limited by how much information you can put in the prompt (because the context window is fixed). Association for Computational Linguistics. Indo-European, Sino-Tibetan).[

Natural Language Processing 59

Natural Language Processing Computational Linguistics NLP ChatGPT 59

Viso.ai

JULY 18, 2024

LLMs changed how we interact with the internet as finding relevant information or performing specific tasks was never this easy before. 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).

Large Language Models 52

Large Language Models Neural Network Natural Language Processing BERT 52

Explosion

DECEMBER 28, 2019

Ines’ talk in the language track, “Practical Transfer Learning for NLP with spaCy and Prodigy” , focused on the increasing trend of initializing models with information from large raw-text corpora, and how you can use this type of technique in spaCy and Prodigy. ✨ Mar 20: A few days later, we upgraded Prodigy to v1.8 to support spaCy v2.1.

NLP 52

NLP BERT Machine Learning Python 52

Sebastian Ruder

NOVEMBER 14, 2022

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. Practical data   For new datasets, it is thus ever more important to create data that is informed by real-world usage.

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Sebastian Ruder

JANUARY 24, 2022

6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. Alternatively, FNet [27] uses 1D Fourier Transforms instead of self-attention to mix information at the token level. The latter have been scaled beyond the controlled environment of ImageNet to random collections of images [5]. What’s next?

NLP 52

NLP ML BERT Computational Linguistics 52

The Stanford AI Lab Blog

JANUARY 21, 2022

Conference of the North American Chapter of the Association for Computational Linguistics. ↩ Devlin, J., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. RoBERTa: A Robustly Optimized BERT Pretraining Approach. Neural Information Processing Systems. ↩ Finn, C.,

Robotics 40

Robotics Computational Linguistics BERT Computer Vision 40

Applied Data Science

DECEMBER 23, 2022

As people played around with them, many problems such as bias and mis-information became prevalent. The first computational linguistics methods tried to bypass the immense complexity of human language learning by hard-coding syntax and grammar rules in their models. Some of this year’s breakthroughs created controversies.

Neural Network 52

Neural Network Data Science AI Deep Learning 52

Sebastian Ruder

DECEMBER 6, 2021

Reading Comprehension assumes a gold paragraph is provided Standard approaches for reading comprehension build on pre-trained models such as BERT. Using BERT for reading comprehension involves fine-tuning it to predict a) whether a question is answerable and b) whether each token is the start and end of an answer span.

BERT 52

BERT NLP Natural Language Processing Computational Linguistics 52

NLP People

JULY 24, 2019

The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. Especially pre-trained word embeddings such as Word2Vec, FastText and BERT allow NLP developers to jump to the next level. Transfer learning is another approach to reusing models across different tasks.

NLP 52

NLP Neural Network Deep Learning Natural Language Processing 52