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The AI Index 2022 Annual Report
Welcome to the fifth edition of the AI Index Report! The latest edition includes data from a broad set of academic, private, and nonprofit organizations as well as more self-collected data and…
Predicting Twitter Engagement With Deep Language Models
This work first fine-tune leading multilingual language models M-BERT and XLM-R for Twitter data, and Embeddings from these models are used to extract tweet and user history representations, which combine all components together and jointly train them to maximize engagement prediction accuracy.
Mitigating harm in language models with conditional-likelihood filtration
This work presents a methodology for programmatically identifying and removing harmful text from web-scale datasets and discusses the generalization of this method and how trigger phrases reflecting specific values can be used by researchers to build language models which are more closely aligned with their values.
No News is Good News: A Critique of the One Billion Word Benchmark
It is suggested that the temporal nature of news and its distribution shift over time makes it poorly suited for measuring language modeling ability, and potential impact and considerations for researchers building language models and evaluation datasets are discussed.
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measurements
Evaluate is a library to support best practices for measurements, metrics, and comparisons of data and models, and Evaluation on the Hub is a platform that enables the large-scale evaluation of over 75,000 models and 11,000 datasets on the Hugging Face Hub, for free, at the click of a button.