Papers with CRT
Provably Confidential Language Modelling (2022.naacl-main)
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| Challenge: | Existing methods to train language models without memorizing sensitive data are mismatched and can be difficult to screen and filter. |
| Approach: | They propose a method to train language generation models while protecting the confidential segments of training data. |
| Outcome: | The proposed method prevents unintended memorization by randomizing parts of the training process while protecting strong confidentiality. |
CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) show powerful reasoning abilities on text-based tasks, but their reasoning capability on structured data such as tables has not been systematically explored. |
| Approach: | They first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis and then construct a complex reasoning QA dataset over tabular dataset. |
| Outcome: | The proposed method is able to solve table reasoning tasks without handcrafted demonstrations. |