Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alexander Tselousov, Thomas Wolf
| Challenge: | Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks. |
| Approach: | They propose to apply large-scale pretrained language models to natural language generation tasks by comparing architectural and training schemes. |
| Outcome: | The proposed architectures perform well on open-domain dialog as a typical high entropy generation task. |
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| Challenge: | a tutorial on scaling natural language processing will recapitulate the state-of-the-art in the field . |
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A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)
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Transfer Learning in Natural Language Processing (N19-5)
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| Challenge: | a recent paper describes efficient deep neural network architectures for expanding natural language capabilities of virtual agents. |
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The Amazing World of Neural Language Generation (2020.emnlp-tutorials)
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Progressive Generation of Long Text with Pretrained Language Models (2021.naacl-main)
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| Challenge: | Existing methods for "long" text generation are limited to outputs of 50-200 tokens . however, our proposed ProGen generates coherent long passages of text in a progressive manner . |
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