Papers with task predictions
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages . |
| Approach: | They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages. |
| Outcome: | The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases. |
Predicting Through Generation: Why Generation Is Better for Prediction (2025.acl-long)
Copied to clipboard
Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat, Chun-Nam Yu, Mojtaba Soltanalian, Ivan Garibay, Ozlem Garibay, Chen Chen, Niloofar Yousefi
| Challenge: | Large Language Models (LLMs) are increasingly used for predictive tasks such as classification and regression. |
| Approach: | They propose a framework that generates output tokens from mas-sive text corpora and a task adapter to ensure consistency between token generation and final prediction. |
| Outcome: | The proposed framework outperforms baseline models on classification and regression benchmarks and the proposed framework consistently outperformed standard baseline models. |