Does Pretraining for Summarization Require Knowledge Transfer? (2021.findings-emnlp)
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| Challenge: | Existing theories claim that pretraining models learn linguistic knowledge from the pretraining corpus, but scientific explanations for these benefits remain unknown. |
| Approach: | They propose to use random character n-grams to test models on real corpora to see if the small residual benefit of using real data could be accounted for by the structure of the pretraining task. |
| Outcome: | The proposed task performs on documents consisting of character n-grams, whereas pretrained models perform on real corpora with no residual benefit. |
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| Challenge: | Using pre-trained language models, we can apply them to specialized domains such as scientific articles or clinical data. |
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| Challenge: | Large language models are increasingly adopted for handling structured data, despite pretraining on unstructured text. |
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
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Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, Samuel R. Bowman
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To Pretrain or Not to Pretrain: Examining the Benefits of Pretrainng on Resource Rich Tasks (2020.acl-main)
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| Challenge: | Existing studies on pretraining NLP models with variants of Masked Language Model (MLM) objectives have shown that the number of training samples used in the downstream task is limited. |
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Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
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When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization (2023.eacl-main)
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Faisal Ladhak, Esin Durmus, Mirac Suzgun, Tianyi Zhang, Dan Jurafsky, Kathleen McKeown, Tatsunori Hashimoto
| Challenge: | Existing studies have shown that large language models contain linguistic and societal biases, but it is unclear how these biase amplify to downstream tasks. |
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Deciphering the Impact of Pretraining Data on Large Language Models through Machine Unlearning (2024.findings-acl)
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| Challenge: | Existing studies have suggested that the composition of the pretraining corpus exerts a significant impact upon the performance of LLMs. |
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