47 citations · 138 across the 18 of their papers we have counts for
20 papers
The Hallucinations Leaderboard -- An Open Effort to Measure Hallucinations in Large Language Models
Giwon Hong, Aryo Pradipta Gema, Rohit Saxena +8
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text. However, the…
Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
Zhuoming Chen, Avner May, Ruslan Svirschevski +4
As the usage of large language models (LLMs) grows, performing efficient inference with these models becomes increasingly important. While speculative decoding has recently emerged…
Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements
Anton Voronov, Lena Wolf, Max Ryabinin
Large language models demonstrate a remarkable capability for learning to solve new tasks from a few examples. The prompt template, or the way the input examples are formatted to o…
Distributed Inference and Fine-tuning of Large Language Models Over The Internet
Alexander Borzunov, Max Ryabinin, Artem Chumachenko +5
Large language models (LLMs) are useful in many NLP tasks and become more capable with size, with the best open-source models having over 50 billion parameters. However, using thes…
Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet Hierarchy
Anton Baryshnikov, Max Ryabinin
Text-to-image synthesis has recently attracted widespread attention due to rapidly improving quality and numerous practical applications. However, the language understanding capabi…
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
Ying Sheng, Lianmin Zheng, Binhang Yuan +11
The high computational and memory requirements of large language model (LLM) inference make it feasible only with multiple high-end accelerators. Motivated by the emerging demand f…
Is This Loss Informative? Faster Text-to-Image Customization by Tracking Objective Dynamics
Anton Voronov, Mikhail Khoroshikh, Artem Babenko +1
Text-to-image generation models represent the next step of evolution in image synthesis, offering a natural way to achieve flexible yet fine-grained control over the result. One em…
SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient
Max Ryabinin, Tim Dettmers, Michael Diskin +1
Many deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clus…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
RuCoLA: Russian Corpus of Linguistic Acceptability
Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin +3
Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering impl…
Petals: Collaborative Inference and Fine-tuning of Large Models
Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers +5
Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download…
Training Transformers Together
Alexander Borzunov, Max Ryabinin, Tim Dettmers +5
The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and instituti…
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees
Aleksandr Beznosikov, Peter Richtárik, Michael Diskin +2
Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transp…
It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning
Alexey Tikhonov, Max Ryabinin
Commonsense reasoning is one of the key problems in natural language processing, but the relative scarcity of labeled data holds back the progress for languages other than English.…
Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin +1
Many areas of deep learning benefit from using increasingly larger neural networks trained on public data, as is the case for pre-trained models for NLP and computer vision. Traini…
Distributed Deep Learning in Open Collaborations
Michael Diskin, Alexey Bukhtiyarov, Max Ryabinin +13
Modern deep learning applications require increasingly more compute to train state-of-the-art models. To address this demand, large corporations and institutions use dedicated High…
Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets
Max Ryabinin, Andrey Malinin, Mark Gales
Ensembles of machine learning models yield improved system performance as well as robust and interpretable uncertainty estimates; however, their inference costs may often be prohib…
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
Max Ryabinin, Eduard Gorbunov, Vsevolod Plokhotnyuk +1
Training deep neural networks on large datasets can often be accelerated by using multiple compute nodes. This approach, known as distributed training, can utilize hundreds of comp…
Embedding Words in Non-Vector Space with Unsupervised Graph Learning
Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova +1
It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe). While this approach is convenient, it is unnatural for language: words form a…
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts
Max Ryabinin, Anton Gusev
Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. F…