In the new paper Training Compute-Optimal Large Language Models, a DeepMind research team posits that current large language models are significantly undertrained and, based on empirical outcomes of over 400 training runs, proposes three predictive approaches for optimally setting model size and training duration.
In the new paper Token Dropping for Efficient BERT Pretraining, a research team from Google, New York University, and the University of Maryland proposes a simple but effective “token dropping” technique that significantly reduces the pretraining cost of transformer models such as BERT without hurting performance on downstream fine-tuning tasks.
A team from Google Research and the Swiss AI Lab IDSIA proposes the Block-Recurrent Transformer, a novel long-sequence processing approach that has the same computation time and parameter count costs as a conventional transformer layer but achieves significant perplexity improvements in language modelling tasks over very long sequences.
A research team from Sapienza University and OpenAI introduces an explanatory learning procedure that enables machines to understand existing explanations from symbolic sequences and create new explanations for unexplained phenomena, and further proposes Critical Rationalist Network (CRN) models for discovering explanations for novel phenomena.
A Google AI research team explores zero-label learning (training with synthetic data only) in natural language processing, and introduces Unsupervised Data Generation (UDG), a training data creation procedure designed to synthesize high-quality training data without human annotations.
MIT researchers present an automated, objective and transparent data-driven method for measuring media bias. The study analyses roughly a million articles from about a hundred newspapers for bias on various news topics, maps the newspapers into a two-dimensional media bias landscape, and shows that the data-driven results agree well with human-judgement classifications.
A Google Research team explores the design space of Transformer models in an effort to enable deep learning architectures to solve compositional tasks. The proposed approach provides models with inductive biases via design decisions that significantly impact compositional generalization, and achieves state-of-the-art results on the COGS and PCFG composition benchmarks.
A Google Research team draws inspiration from two numerical analysis methods — Hierarchical Matrix (H-Matrix) and Multigrid — to address the quadratic complexity problem of attention mechanisms in transformer architectures, proposing a hierarchical attention scheme that has linear complexity in run time and memory.
A research team from the University of Melbourne, Facebook AI, and Twitter Cortex proposes a black-box test method for assessing and debugging the numerical translation of neural machine translation systems in a systematic manner. The approach reveals novel types of errors that are general across multiple state-of-the-art translation systems.
A Google Research team proposes Wordcraft, a text editor with a built-in AI-powered creative writing assistant. Wordcraft uses few-shot learning and the natural affordances of conversation to support a variety of user interactions; and can help with story planning, writing and editing.
A research team from Baidu proposes ERNIE 3.0, a unified framework for pretraining large-scale, knowledge-enhanced models that can easily be tailored for both natural language understanding and generation tasks with zero-shot learning, few-shot learning or fine-tuning, and achieves state-of-the-art results on NLP tasks.
A research team from ByteDance AI Lab, University of Wisconsin–Madison and Nanjing University wins the ACL 2021 best paper award. Their proposed Vocabulary Learning via Optimal Transport (VOLT) approach leverages optimal transport to automatically find an optimal vocabulary without trial training.
A research team from UC Davis, Microsoft Research and Johns Hopkins University extends work on training massive amounts of linguistic data to reveal the grammatical structures in their representations to the domain of mathematical reasoning, showing that both the standard transformer and the TP-Transformer can compose the meanings of mathematical symbols based on their structured relationships.
A research team from McGill University, Mila – Quebec AI Institute and Facebook AI proposes novel metrics and perturbation functions to detect, quantify and compare trade-offs between robustness and faithfulness in NMT systems, both on the corpus level and with particular examples.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
Microsoft’s new tunable gigaword-scale neural network DialogGPT is a virtual master of conversation that outperforms strong baseline systems in generating relevant and context-consistent responses and attains near human level performance in conversational response generation tasks.
“AI is the best bot to keep people safe on our platforms,” Facebook Director of Artificial Intelligence Manohar Paluri told the F8 audience, adding that an effective way to achieve that goal is enabling Facebook’s AI system to “understand content and work effectively with less labeled training data.”
Google is looking to expand its AI research activities in the Japanese capital. The company’s deep learning and AI research team Google Brain yesterday posted a “Tokyo job listing seeking talented experts to participate in cutting edge research on machine learning”.