Papers by Tim Dettmers
High Performance Natural Language Processing (2020.emnlp-tutorials)
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| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
| Approach: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
| Outcome: | This cutting-edge tutorial recapitulates the state-of-the-art in natural language processing with scale in perspective. |
Jack the Reader – A Machine Reading Framework (P18-4)
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Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rocktäschel, Matko Bošnjak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
| Challenge: | Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. |
| Approach: | They propose a framework for Machine Reading that allows for quick prototyping by component reuse and evaluation of new models on existing datasets. |
| Outcome: | The proposed framework supports question answering, natural language inference and link prediction tasks. |
Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model (2023.emnlp-main)
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| Challenge: | Large and sparse feed-forward layers (S-FFN) have proven effective in scaling up the model size for pretraining large language models. |
| Approach: | They compare S-FFN architectures for language modeling and compare their performance and efficiency . they found a simpler selection method that selects blocks through their mean aggregated hidden states . |
| Outcome: | The proposed model size and selection method achieve lower perplexity in language model pretraining compared to existing MoE architectures. |
Petals: Collaborative Inference and Fine-tuning of Large Models (2023.acl-demo)
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Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Maksim Riabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, Colin Raffel
| Challenge: | Recent studies show that pretrained language models can solve practical tasks using more than 100 billion parameters. |
| Approach: | They propose a system for inference and fine-tuning of large models collaboratively by joining the resources of multiple parties. |
| Outcome: | The proposed system outperforms offloading for very large models running on consumer GPUs with 1 step per second, enough for many interactive LLM applications. |