Papers with baseline language model
Transcending Scaling Laws with 0.1% Extra Compute (2023.emnlp-main)
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Yi Tay, Jason Wei, Hyung Chung, Vinh Tran, David So, Siamak Shakeri, Xavier Garcia, Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc Le, Mostafa Dehghani
| Challenge: | Existing scaling of language models is expensive and requires significant computational costs. |
| Approach: | They propose a method that substantially improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
| Outcome: | The proposed method significantly improves existing language models and their scaling curves with a relatively tiny amount of extra compute. |
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts (2022.emnlp-main)
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| Challenge: | Existing approaches to end-to-end questionanswering assume that pre-trained language can decompose complex tasks into more straightforward sub-tasks. |
| Approach: | They propose to use distant supervision to train decomposition-based transformers for large-scale parallel news. |
| Outcome: | The proposed model improves on semantic parsing and on hotpotQA and strategyQA datasets by 20% to 30%. |